DISTRIBUTED REAL TIME SPEECH RECOGNITION SYSTEM

Abstract: A real-time system (100) incorporating speech recognition and linguistic processing for recognizing a spoken query by a user and distributed between client (150) and server (180), is disclosed. The system (100) accepts user's queries in the form of speech at the client (150) where minimal processing extracts a sufficient number of acoustic speech vectors representing the utterance. These vectors are sent via a communications channel (160A) to the server (180) where additional acoustic vectors are derived. Using Hidden Markov Models (HMMs), and appropriate grammars and dictionaries conditioned by the selections made by the user, the speech representing the user's query is fully decoded into text (or some other suitable form) at the server (180). The text corresponding to the user's query is then simultaneously sent to a natural language engine (190) and a database processor (186) where optimized SQL statements are constructed for a full-text search from a database (188) for a record set of several stored questions that best matches the user's query. Further processing in the natural language engine (190) narrows the search to a single stored question. The answer corresponding to this single stored question is next retrieved from the file path and sent to the client (150) in compressed form. At the client (150), the answer to the user's query is articulated to the user using a text-to-speech engine (159) in his or her native natural language. The system (100) requires no training and can operate in several natural languages.

Description: DISTRIBUTED REAL TIME SPEECH RECOGNITION SYSTEM RELATED APPLICATIONS The present application is related to the following applications also filed contemporaneously herewith: 1) Speech Based Learning/Training system, attorney docket no. PHO 99-002; 2) Internet Server with Speech Support for Enhanced Interactivity — attorney docket no. PHO 99-003; 3) Intelligent Query Engine For Processing Voice Based Queries — attorney docket no. PHO 99-004; The above are incorporated by reference herein. FIELD OF THE INVENTION The invention relates to a system and an interactive method for responding to speech based user inputs and queries presented over a distributed network such as the Internet or local intranet. This interactive system when implemented over the worldwide web services (WWW) of the Internet, functions so that a client or user can ask a question in a natural language such as English, French, German, Spanish or Japanese and receive the appropriate answer at his or her computer or accessory also in his or her native natural language. The system has particular applicability to such applications as remote learning, e-commerce, technical e-support services, Internet searching, etc. BACKGROUND OF THE INVENTION The Internet, and in particular, the World-Wide Web (WWW), is growing in popularity and usage for both commercial and recreational purposes, and this trend is expected to continue. This phenomenon is being driven, in part, by the increasing and widespread use of personal computer systems and the availability of low cost Internet access. The emergence of inexpensive Internet access devices and high speed access techniques such as ADSL, cable modems, satellite modems, and the like, are expected to further accelerate the mass usage of the WWW. Accordingly, it is expected that the number of entities offering services, products, etc., over the WWW will increase dramatically over the coming years. Until now, however, the internet "experience" for users has been limited mostly to non-voice based input/output devices, such as keyboards, intelligent electronic pads, mice, trackballs, printers, monitors, etc. This presents somewhat of a bottleneck for interacting over the WWW for a variety of reasons. First, there is the issue of familiarity. Many kinds of applications lend themselves much more naturally and fluently to a voice-based environment. For instance, most people shopping for audio recordings are very comfortable with asking a live sales clerk in a record store for information on titles by a particular author, where they can be found in the store, etc. While it is often possible to browse and search on one's own to locate items of interest, it is usually easier and more efficient to get some form of human assistance first, and, with few exceptions, this request for assistance is presented in the form of a oral query. In addition, many persons cannot or will not, because of physical or psychological barriers, use any of the aforementioned conventional I/O devices. For example, many older persons cannot easily read the text presented on WWW pages, or understand the layout/hierarchy of menus, or manipulate a mouse to make finely coordinated movements to indicate their selections. Many others are intimidated by the look and complexity of computer systems, WWW pages, etc., and therefore do not attempt to use online services for this reason as well. Thus, applications which can mimic normal human interactions are likely to be preferred by potential on-line shoppers and persons looking for information over the WWW. It is also expected that the use of voice-based systems will increase the universe of persons willing to engage in e-commerce, e-learning, etc. To date, however, there are very few systems, if any, which permit this type of interaction, and, if they do, it is very limited. For example, various commercial programs sold by IBM (Via Voice) and Kurzweil (Dragon) permit some user control of the interface (opening, closing files) and searching (by using previously trained URLs) but they do not present a flexible solution that can be used by a number of users across multiple cultures and without time consuming voice training. Typical prior efforts to implement voice based functionality in an internet context can be seen in U.S. Patent no. 5,819,220 incorporated by reference herein. Another issue presented by the lack of voice-based systems is efficiency. Many companies are now offering technical support over the Internet, and some even offer live operator assistance for such queries. While this is very advantageous (for the reasons mentioned above) it is also extremely costly and inefficient, because a real person must be employed to handle such queries. This presents a practical limit that results in long wait times for responses or high labor overheads. An example of this approach can be seen U.S. Patent no. 5,802,526 also incorporated by reference herein. In general, a service presented over the WWW is far more desirable if it is "scalable," or, in other words, able to handle an increasing amount of user traffic with little if any perceived delay or troubles by a prospective user. In a similar context, while remote learning has become an increasingly popular option for many students, it is practically impossible for an instructor to be able to field questions from more than one person at a time. Even then, such interaction usually takes place for only a limited period of time because of other instructor time constraints. To date, however, there is no practical way for students to continue a human-like question and answer type dialog after the learning session is over, or without the presence of the instructor to personally address such queries. Conversely, another aspect of emulating a human-like dialog involves the use of oral feedback. In other words, many persons prefer to receive answers and information in audible form. While a form of this functionality is used by some websites to communicate information to visitors, it is not performed in a real-time, interactive question-answer dialog fashion so its effectiveness and usefulness is limited. Yet another area that could benefit from speech-based interaction involves so- called "search" engines used by Internet users to locate information of interest at web sites, such as the those available at Yahoo.com, metacrarvler.com, Excite.com, etc. These tools permit the user to form a search query using either combinations of keywords or metacategories to search through a web page database containing text indices associated with one or more distinct web pages. After processing the user's request, therefore, the search engine returns a number of hits which correspond, generally, to URL pointers and text excerpts from the web pages that represent the closest match made by such search engine for the particular user query based on the search processing logic used by search engine. The structure and operation of such prior art search engines, including the mechanism by which they build the web page database, and parse the search query, are well known in the art. To date, applicant is unaware of any such search engine that can easily and reliably search and retrieve information based on speech input from a user. There are a number of reasons why the above environments (e-commerce, e- support, remote learning, internet searching, etc.) do not utilize speech-based interfaces, despite the many benefits that would otherwise flow from such capability. First, there is obviously a requirement that the output of the speech recognizer be as accurate as possible. One of the more reliable approaches to speech recognition used at this time is based on the Hidden Markov Model (HMM) - a model used to mathematically describe any time series. A conventional usage of this technique is disclosed, for example, in U.S. Patent No. 4,587,670 incorporated by reference herein. Because speech is considered to have an underlying sequence of one or more symbols, the HMM models corresponding to each symbol are trained on vectors from the speech waveforms. The Hidden Markov Model is a finite set of states, each of which is associated with a (generally multidimensional) probability distribution. Transitions among the states are governed by a set of probabilities called transition probabilities. In a particular state an outcome or observation can be generated, according to the associated probability distribution. This finite state machine changes state once every time unit, and each time t such that a state j is entered, a spectral parameter vector O. is generated with probability density B,(O t ). It is only the outcome, not the state visible to an external observer and therefore states are "hidden" to the outside; hence the name Hidden Markov Model. The basic theory of HMMs was published in a series of classic papers by Baum and his colleagues in the late 1960's and early 1 70's. HMMs were first used in speech applications by Baker at Carnegie Mellon, by Jelenik and colleagues at IBM in the late 1970's and by Steve Young and colleagues at Cambridge University, UK in the 1990's. Some typical papers and texts are as follows: 1. L.E. Baum, T. Petrie, "Statistical inference for probabilistic functions for finite state Markov chains", Ann. Math. Stat., 37: 1554-1563, 1966 2. L.E. Baum, "An inequality and associated maximation technique in statistical estimation for probabilistic functions of Markov processes", Inequalities 3: 1- 8, 1972 3. J.H. Baker, "The dragon system — An Overview", IEEE Trans, on ASSP Proc, ASSP-23(1): 24-29, Feby. 1975 4. F. Jeninek et al, "Continuous Speech Recognition: Statistical methods" in Handbook of Statistics, II, P.R. Kristnaiad, Ed. Amsterdam, The Netherlands, North-Holland, 1982 5. L.R. Bahl, F. Jeninek, R.L. Mercer, "A maximum likelihood approach to continuous speech recognition", IEEE Trans. Pattern Anal. Mach. Intell., PAMI-5: 179-190, 1983 6. J.D. Ferguson, "Hidden Markov Analysis: An Introduction", in Hidden Markov Models for Speech, Institute of Defense Analyses, Princeton, NJ. 1980. 7. H.R. Rabiner and B.H. Juang, "Fundamentals of Speech Recognition", Prentice Hall, 1993 8. H.R. Rabiner, "Digital Processing of Speech Signals", Prentice Hall, 1978 More recently research has progressed in extending HMM and combining HMMs with neural networks to speech recognition applications at various laboratories. The following is a representative paper: 9. Nelson Morgan, Herve Bourlard, Steve Renals, Michael Cohen and Horacio Franco (1993), Hybrid Neural Network/Hidden Markov Model Systems for Continuous Speech Recognition. Journal of Pattern Recognition and Artificial Intelligence, Vol. 7, No. 4 pp. 899-916. Also in I. Guyon and P. Wang editors, Advances in Pattern Recognition Systems using Neural Networks, Vol. 7 of a Series in Machine Perception and Artificial Intelligence. World Scientific, Feb. 1994. All of the above are hereby incorporated by reference. While the HMM-based speech recognition yields very good results, contemporary variations of this technique cannot guarantee a word accuracy requirement of 100% exactiy and consistently, as will be required for WWW applications for all possible all user and environment conditions. Thus, although speech recognition technology has been available for several years, and has improved significantly, the technical requirements have placed severe restrictions on the specifications for the speech recognition accuracy that is required for an application that combines speech recognition and natural language processing to work satisfactorily. In contrast to word recognition, Natural language processing (NLP) is concerned with the parsing, understanding and indexing of transcribed utterances and larger linguistic units. Because spontaneous speech contains many surface phenomena such as disfluencies, - hesitations, repairs and restarts, discourse markers such as ''well and other elements which cannot be handled by the typical speech recognizer, it is the problem and the source of the large gap that separates speech recognition and natural language processing technologies. Except for silence between utterances, another problem is the absence of any marked punctuation available for segmenting the speech input into meaningful units such as utterances. For optimal NLP performance, these types of phenomena should be annotated at its input. However, most continuous speech recogniuon systems produce only a raw sequence of words. An example of conventional systems using NLP are shown in U S Patent Nos 4,991,094, 5,068,789, 5,146,405 and 5,680,628, all of which are incorporated by reference herein. Second, most of the very reliable voice recognition systems are speaker- dependent, requiring that the interface be "trained" with the user's voice, which takes a lot of time, and is thus very undesirable from the perspective of a WWW environment, where a user may interact only a few times with a particular website Furthermore, speaker-dependent systems usually require a large user dictionary (one for each unique user) which reduces the speed of recognition. This makes it much harder to implement a real-time dialog interface with satisfactory response capability (i.e , something that mirrors normal conversation — on the order of 3 — 5 seconds is probably ideal). At present, the typical shrink-wrapped speech recognition application software include offering s from IBM (ViaVoice) and Dragon Systems (Dragon). While most of these applications are adequate for dictation and other transcribing applications, they are woefully inadequate for applications such as NLQS where the word error rate must be close to 0% In addition these offerings require long training times and are typically are non client-server configurations. Other types of trained systems are discussed in U.S. Patent No 5,231,670 assigned to Kurzweil, and which is also incorporated by reference herein Another significant problem faced in a distributed voice-based system is a lack of uniformity/control in the speech recognition process. In a typical stand-alone implementation of a speech recognition system, the entire SR engine runs on a single client A well-known system of this type is depicted in U S Patent No. 4,991,217 incorporated by reference herein. These clients can take numerous forms (desktop PC, laptop PC, PDA, etc ) having varying speech signal processing and communications capability Thus, from the server side perspective, it is not easy to assure uniform treatment of all users accessing a voice-enabled web page, since such users may have significantly disparate word recognition and error rate performances. While a prior art reference to Gould et. al. — U S Patent No. 5915236 - discusses generally the notion of tailoring a recognition process to a set of available computational resources, it does not address or attempt to solve the issue of how to optimize resources in a distributed environment such as a client-server model Again, to enable such voice-based technologies on a wide-spread scale it is far more preferable to have a system that harmonizes and accounts for discrepancies in individual systems so that even the thinnest client is supportable, and so that all users are able to interact in a satisfactory manner with the remote server running the e-commerce, e-support and/or remote learning application. Two references that refer to a distributed approach for speech recognition include US Patents 5956683 and 5960399 incorporated by reference herein. In the first of these, US Patent 5956683 — Distributed Voice Recognition System (assigned to Qualcomm) an implementation of a distributed voice recognition system between a telephony-based handset and a remote station is described. In this implementation, all of the word recognition operations seem to take place at the handset. This is done since the patent describes the benefits that result from locating of the system for acoustic feature extraction at the portable or cellular phone in order to limit degradation of the acoustic features due to quantization distortion resulting from the narrow bandwidth telephony channel. This reference therefore does not address the issue of how to ensure adequate performance for a very thin client platform. Moreover, it is difficult to determine, how, if at all, the system can perform real-time word recognition, and there is no meaningful description of how to integrate the system with a natural language processor. The second of these references - US Patent 5960399 — Client/Server Speech Processor/Recognizer (assigned to GTE) describes the implementation of a HMM-based distributed speech recognition system. This reference is not instructive in many respects, however, including how to optimize acoustic feature extraction for a variety of client platforms, such as by performing a partial word recognition process where appropriate. Most importantly, there is only a description of a primitive server-based recognizer that only recognizes the user's speech and simply returns certain keywords such as the user's name and travel destination to fill out a dedicated form on the user's machine. Also, the streaming of the acoustic parameters does not appear to be implemented in real-time as it can only take place after silence is detected. Finally, while the reference mentions the possible use of natural language processing (column 9) there is no explanation of how such function might be implemented in a real-time fashion to provide an interactive feel for the user. SUMMARY OF THE INVENTION An object of the present invention, therefore, is to provide an improved system and method for overcoming the limitations of the prior art noted above; A primary object of the present invention is to provide a word and phrase recognition system that is flexibly and optimally distributed across a client/platform computing architecture, so that improved accuracy, speed and uniformity can be achieved for a wide group of users, A further object of the present invention is to provide a speech recognition system that efficiently integrates a distributed word recognition system with a natural language processing system, so that both individual words and entire speech utterances can be quickly and accurately recognized in any number of possible languages, A related object of the present invention is to provide an efficient query response system so that an extremely accurate, real-time set of appropriate answers can be given in response to speech-based queries, Yet another object of the present invention is to provide an interactive, real-time instructional/learning system that is distributed across a client/server architecture, and permits a real-time question/answer session with an interactive character, A related object of the present invention is to implement such interactive character with an articulated response capability so that the user experiences a humanlike interaction, Still a further object of the present invention is to provide an internet website with speech processing capability so that voice based data and commands can be used to interact with such site, thus enabling voice-based e-commerce and e-support services to be easily scaleable, Another object is to implement a distributed speech recognition system that utilizes environmental variables as part of the recognition process to improve accuracy and speed, A further object is to provide a scaleable query/response database system, to support any number of query topics and users as needed for a particular application and instantaneous demand, Yet another object of the present invention is to provide a query recognition system that employs a two-step approach, including a relatively rapid first step to narrow down the list of potential responses to a smaller candidate set, and a second more computationally intensive second step to identify the best choice to be returned in response to the query from the candidate set, A further object of the present invention is to provide a natural language processing system that facilitates query recognition by extracting lexical components of speech utterances, which components can be used for rapidly identifying a candidate set of potential responses appropriate for such speech utterances; Another related object of the present invention is to provide a natural language processing system that facilitates query recognition by comparing lexical components of speech utterances with a candidate set of potential response to provide an extremely accurate best response to such query. One general aspect of the present invention, therefore, relates to a natural language query system (NLQS) that offers a fully interactive method for answering user's questions over a distributed network such as the Internet or a local intranet. This interactive system when implemented over the worldwide web (WWW) services of the Internet functions so that a client or user can ask a question in a natural language such as English, French, German or Spanish and receive the appropriate answer at his or her personal computer also in his or her native natural language. The system is distributed and consists of a set of integrated software modules at the client's machine and another set of integrated software programs resident on a server or set of servers The client-side software program is comprised of a speech recognition program, an agent and its control program, and a communication program. The server- side program is comprised of a communication program, a natural language engine (NLE), a database processor (DBProcess), an interface program for interfacing the DBProcess with the NLE, and a SQL database. In addition, the client's machine is equipped with a microphone and a speaker. Processing of the speech utterance is divided between the client and server side so as to optimize processing and transmission latencies, and so as to provide support for even very thin client platforms. In the context of an interactive learning application, the system is specifically used to provide a single-best answer to a user's question. The question that is asked at the client's machine is articulated by the speaker and captured by a microphone that is built in as in the case of a notebook computer or is supplied as a standard peripheral attachment Once the question is captured, the question is processed partially by NLQS client-side software resident in the client's machine. The output of this partial processing is a set of speech vectors that are transported to the server via the Internet to complete the recognition of the user's questions This recognized speech is then converted to text at the server After the user's question is decoded by the speech recognition engine located at the server, the question is converted to a structured query language (SQL) query. This query is then simultaneously presented to a software process within the server called DBProcess for preliminary processing and to a Natural Language Engine (NLE) module for extracting the noun phrases (NP) of the user's question. During the process of extracting the noun phrase within the NLE, the tokens of the users' question are tagged The tagged tokens are then grouped so that the NP list can be determined. This information is stored and sent to the DBProcess process In the DBProcess, the SQL query is fully customized using the NP extracted from the user's question and other environment variables that are relevant to the application. For example, in a training application, the user's selection of course, chapter and or section would constitute the environment variables. The SQL query is constructed using the extended SQL Full-Text predicates - CONTAINS, FREETEXT, NEAR, AND. The SQL query is next sent to the Full-Text search engine within the SQL database, where a Full-Text search procedure is initiated. The result of this search procedure is recordset of answers. This recordset contains stored questions that are similar linguistically to the user's question. Each of these stored questions has a paired answer stored in a separate text file, whose path is stored in a table of the database. The entire recordset of returned stored answers is then returned to the NLE engine in the form of an array. Each stored question of the array is then linguistically processed sequentially one by one. This linguistic processing constitutes the second step of a 2-step algorithm to determine the single best answer to the user's question. This second step proceeds as follows: for each stored question that is returned in the recordset, a NP of the stored question is compared with the NP of the user's question. After all stored questions of the array are compared with the user's question, the stored question that yields the maximum match with the user's question is selected as the best possible stored question that matches the user's question. The metric that is used to determine the best possible stored question is the number of noun phrases. The stored answer that is paired to the best-stored question is selected as the one that answers the user's question. The ID tag of the question is then passed to the DBProcess. This DBProcess the returns the answer which is stored in a file. A communication link is again established to send the answer back to the client in compressed form. The answer once received by the client is decompressed and articulated to the user by the text-to-speech engine. Thus, the invention can be used in any number of different applications involving interactive learning systems, internet related commerce sites, internet search engines, etc. Computer-assisted instruction environments often require the assistance of mentors or live teachers to answer questions from students. This assistance often takes the form of organizing a separate pre-arranged forum or meeting time that is set aside for chat sessions or live call-in sessions so that at a scheduled time answers to questions may be provided. Because of the time immediacy and the on-demand or asynchronous nature of on-line training where a student may log on and take instruction at any time and at any location, it is important that answers to questions be provided in a timely and cost- effective manner so that the user or student can derive the maximum benefit from the material presented This invention addresses the above issues It provides the user or student with answers to questions that are normally channeled to a live teacher or mentor. This invention provides a single-best answer to questions asked by the student. The student asks the question in his or her own voice in the language of choice The speech is recognized and the answer to the question is found using a number of technologies including distributed speech recognition, full-text search database processing, natural language processing and text-to-speech technologies The answer is presented to the user, as in the case of a live teacher, in an articulated manner by an agent that mimics the mentor or teacher, and in the language of choice - English, French, German, Japanese or other natural spoken language The user can choose the agent's gender as well as several speech parameters such as pitch, volume and speed of the character's voice. Other applications that benefit from NLQS are e-commerce applications. In this application, the user's query for a price of a book, compact disk or for the availability of any item that is to be purchased can be retrieved without the need to pick through various lists on successive web pages Instead, the answer is provided directly to the user without any additional user input. Similarly, it is envisioned that this system can be used to provide answers to frequently-asked questions (FAQs), and as a diagnostic service tool for e-support These questions are typical of a give web site and are provided to help the user find information related to a payment procedure or the specifications of, or problems experienced with a product/ service In all of these applications, the NLQS architecture can be applied. A number of inventive methods associated with these architectures are also beneficially used in a variety of internet related applications. Although the inventions are described below in a set of preferred embodiments, it will be apparent to those skilled in the art the present inventions could be beneficially used in many environments where it is necessary to implement fast, accurate speech recognition, and/or to provide a human-like dialog capability to an intelligent system BRIEF DESCRIPTION OF THE DRAWINGS FIG 1 is a block diagram of a preferred embodiment of a natural language query system (NLQS) of the present invention, which is distributed across a client/server computing architecture, and can be used as an interactive learning system, an e- commerce system, an e-support system, and the like; FIG. 2 is a block diagram of a preferred embodiment of a client side system, including speech capturing modules, partial speech processing modules, encoding modules, transmission modules, agent control modules, and answer/voice feedback modules that can be used in the aforementioned NLQS; FIG. 2 - 2 is a block diagram of a preferred embodiment of a set of initialization routines and procedures used for the client side system of FIG. 2; FIG. 3 is a block diagram of a preferred embodiment of a set of routines and procedures used for handling an iterated set of speech utterances on the client side system of FIG. 2, transmitting speech data for such utterances to a remote server, and receiving appopriate responses back from such server; FIG. 4 is a block diagram of a preferred embodiment of a set of initialization routines and procedures used for un-initializing the client side system of FIG. 2; FIG. 4A is a block diagram of a preferred embodiment of a set of routines and procedures used for implementing a distributed component of a speech recognition module for the server side system of FIG. 5; FIG. 4B is a block diagram of a preferred set of routines and procedures used for implementing an SQL query builder for the server side system of FIG. 5; FIG. 4C is a block diagram of a preferred embodiment of a set of routines and procedures used for implementing a database control process module for the server side system of FIG. 5; FIG. 4D is a block diagram of a preferred embodiment of a set of routines and procedures used for implementing a natural language engine that provides query formulation support, a query response module, and an interface to the database control process module for the server side system of FIG. 5; FIG. 5 is a block diagram of a preferred embodiment of a server side system, including a speech recognition module to complete processing of the speech utterances, environmental and grammer control modules, query formulation modules, a natural language engine, a database control module, and a query response module that can be used in the aforementioned NLQS; FIG. 7 illustrates the organization of a full-text database used as part of server side system shown in FIG. 5; FIG. 7A illustrates the organization of a full-text database course table used as part of server side system shown in FIG. 5 for an interactive learning embodiment of the present invention; FIG. 7B illustrates the organization of a full-text database chapter table used as part of server side system shown in FIG. 5 for an interactive learning embodiment of the present invention; FIG. 7C describes the fields used in a chapter table used as part of server side system shown in FIG. 5 for an interactive learning embodiment of the present invention; FIG. 7D describes the fields used in a section table used as part of server side system shown in FIG. 5 for an interactive learning embodiment of the present invention; FIG. 8 is a flow diagram of a first set of operations performed by a preferred embodiment of a natural language engine on a speech utterance including Tokenization, Tagging and Grouping; FIG. 8A is a flow diagram of the operations performed by a preferred embodiment of a natural language engine on a speech utterance including stemming and Lexical Analysis FIG. 10 is a block diagram of a preferred embodiment of a SQL database search and support system for the present invention; FIGs. 11A - 1 IC are flow diagrams illustrating steps performed in a preferred two step process implemented for query recognition by the NLQS of FIG. 2; FIG. 12 is an illustration of another embodiment of the present invention implemented as part of a Web-based speech based learning/ training System; FIGs. 13 — 17 are illustrations of another embodiment of the present invention implemented as part of a Web-based e-commerce system; FIG. 18 is an illustration of another embodiment of the present invention implemented as part of a voice-based Help Page for an E-Commerce Web Site. DETAILED DESCRIPTION OF THE INVENTION Overview As alluded to above, the present inventions allow a user to ask a question in a natural language such as English, French, German, Spanish or Japanese at a client computing system (which can be as simple as a personal digital assistant or cell-phone, or as sophisticated as a high end desktop PC) and receive an appropriate answer from a remote server also in his or her native natural language. As such, the embodiment of the invention shown in FIG. 1 is beneficially used in what can be generally described as a Natural Language Query System (NLQS) 100, which is configured to interact on a real- time basis to give a human-like dialog capability/experience for e-commerce, e-support, and e-learning applications. The processing for NLQS 100 is generally distributed across a client side system 150, a data link 160, and a server-side system 180. These components are well known in the art, and in a preferred embodiment include a personal computer system 150, an Internet connection 160A, 160B, and a larger scale computing system 180. It will be understood by those skilled in the art that these are merely exemplary components, and that the present invention is by no means limited to any particular implementation or combination of such systems. For example, client-side system 150 could also be implemented as a computer peripheral, a PDA, as part of a cell-phone, as part of an Internet-adapted appliance, an Internet linked kiosk, etc. Similarly, while an Internet connection is depicted for data link 160A, it is apparent that any channel that is suitable for carrying data between client system 150 and server system 180 will suffice, including a wireless link, an RF link, an IR link, a LAN, and the like. Finally, it will be further appreciated that server system 180 may be a single, large-scale system, or a collection of smaller systems interlinked to support a number of potential network users. Initially speech input is provided in the form of a question or query articulated by the speaker at the client's machine or personal accessory as a speech utterance. This speech utterance is captured and partially processed by NLQS client-side software 155 resident in the client's machine. To facilitate and enhance the human-like aspects of the interaction, the question is presented in the presence of an animated character 157 visible to the user who assists the user as a personal information retriever/agent. The agent can also interact with the user using both visible text output on a monitor/ display (not shown) and/or in audible form using a text to speech engine 159. The output of the partial processing done by SRE 155 is a set of speech vectors that are transmitted over communication channel 160 that links the user's machine or personal accessory to a server or servers via the Internet or a wireless gateway that is linked to the Internet as explained above At server 180, the partially processed speech signal data is handled by a server-side SRE 182, which then outputs recognized speech text corresponding to the user's question Based on this user question related text, a text-to-query converter 184 formulates a suitable query that is used as input to a database processor 186. Based on the query, database processor 186 then locates and retrieves an appropriate answer using a customized SQL query from database 188. A Natural Language Engine 190 facilitates structuring the query to database 188. After a matching answer to the user's question is found, the former is transmitted in text form across data link 160B, where it is converted into speech by text to speech engine 159, and thus expressed as oral feedback by animated character agent 157. Because the speech processing is broken up in this fashion, it is possible to achieve real-time, interactive, human-like dialog consisting of a large, controllable set of questions/answers. The assistance of the animated agent 157 further enhances the experience, making it more natural and comfortable for even novice users. To make the speech recognition process more reliable, context-specific grammars and dictionaries are used, as well as natural language processing routines at NLE 190, to analyze user questions lexically While context-specific processing of speech data is known in the art (see e.g., U.S. Patent Nos. 5,960,394, 5,867,817, 5,758,322 and 5,384,892 incorporated by reference herein) the present inventors are unaware of any such implementation as embodied in the present inventions. The text of the user's question is compared against text of other questions to identify the question posed by the user by DB processor/engine (DBE) 186. By optimizing the interaction and relationship of the SR engines 155 and 182, the NLP routines 190, and the dictionaries and grammars, an extremely fast and accurate match can be made, so that a unique and responsive answer can be provided to the user. On the server side 180, interleaved processing further accelerates the speech recognition process. In simplified terms, the query is presented simultaneously both to NLE 190 after the query is formulated, as well as to DBE 186. NLE 190 and SRE 182 perform complementary functions in the overall recognition process. In general, SRE 182 is primarily responsible for determining the identity of the words articulated by the user, while NLE 190 is responsible for the linguistic morphological analysis of both the user's query and the search results returned after the database query. After the user's query is analyzed by NLE 190 some parameters are extracted and sent to the DBProcess. Additional statistics are stored in an array for the 2 nd step of processing During the 2 nd step of 2-step algorithm, the recordset of preliminary search results are sent to the NLE 160 for processing. At the end of this 2 nd step, the single question that matches the user's query is sent to the DBProcess where further processing yields the paired answer that is paired with the single best stored question. Thus, the present invention uses a form of natural language processing (NLP) to achieve optimal performance in a speech based web application system. While NLP is known in the art, prior efforts in Natural Language Processing (NLP) work nonetheless have not been well integrated with Speech Recognition (SR) technologies to achieve reasonable results in a web-based application environment. In speech recognition, the result is typically a lattice of possible recognized words each with some probability of fit with the speech recognizer As described before, the input to a typical NLP system is typically a large linguistic unit The NLP system is then charged with the parsing, understanding and indexing of this large linguistic unit or set of transcribed utterances. The result of this NLP process is to understand lexically or morphologically the entire linguistic unit as opposed to word recognition. Put another way, the linguistic unit or sentence of connected words output by the SRE has to be understood lexically, as opposed to just being "recognized". As indicated earlier, although speech recognition technology has been available for several years, the technical requirements for the NLQS invention have placed severe restrictions on the specifications for the speech recognition accuracy that is required for an application that combines speech recognition and natural language processing to work satisfactorily In realizing that even with the best of conditions, it might be not be possible to achieve the perfect 100% speech recognition accuracy that is required, the present invention employs an algorithm that balances the potential risk of the speech recognition process with the requirements of the natural language processing so that even in cases where perfect speech recognition accuracy is not achieved for each word in the query, the entire query itself is nonetheless recognized with sufficient accuracy. This recognition accuracy is achieved even while meeting very stringent user constraints, such as short latency periods of 3 to 5 seconds (ideally — ignoring transmission latencies which can vary) for responding to a speech-based query, and for a potential set of 100 — 250 query questions This quick response time gives the overall appearance and experience of a real-time discourse that is more natural and pleasant from the user's perspective. Of course, non-real time applications, such as translation services for example, can also benefit from the present teachings as well, since a centralized set of HMMs, grammars, dictionaries, etc., are maintained. General Aspects of Speech Recognition Used in the Present Inventions General background information on speech recognition can be found in the prior art references discussed above and incorporated by reference herein. Nonetheless, a discussion of some particular exemplary forms of speech recognition structures and techniques that are well-suited for NLQS 100 is provided next to better illustrate some of the characteristics, qualities and features of the present inventions. Speech recognition technology is typically of two types — speaker independent and speaker dependent. In speaker-dependent speech recognition technology, each user has a voice file in which a sample of each potentially recognized word is stored. Speaker- dependent speech recognition systems typically have large vocabularies and dictionaries making them suitable for applications as dictation and text transcribing. It follows also that the memory and processor resource requirements for the speaker-dependent can be and are typically large and intensive. Conversely speaker-independent speech recognition technology allows a large group of users to use a single vocabulary file. It follows then that the degree of accuracy that can be achieved is a function of the size and complexity of the grammars and dictionaries that can be supported for a given language. Given the context of applications for which NLQS, the use of small grammars and dictionaries allow speaker independent speech recognition technology to be implemented in NLQS. The key issues or requirements for either type — speaker-independent or speaker- dependent, are accuracy and speed. As the size of the user dictionaries increase, the speech recognition accuracy metric - word error rate (WER) and the speed of recognition decreases. This is so because the search time increases and the pronunciation match becomes more complex as the size of the dictionary increases. The basis of the NLQS speech recognition system is a series of Hidden Markov Models (HMM), which, as alluded to earlier, are mathematical models used to characterize any time varying signal. Because parts of speech are considered to be based on an underlying sequence of one or more symbols, the HMM models corresponding to each symbol are trained on vectors from the speech waveforms. The Hidden Markov Model is a finite set of states, each of which is associated with a (generally multidimensional) probability distribution. Transitions among the states are governed by a set of probabilities called transition probabilities. In a particular state an outcome or observation can be generated, according to an associated probability distribution. This finite state machine changes state once every time unit, and each time t such that a state j is entered, a spectral parameter vector O. is generated with probability density Bj(O t ). It is only the outcome, not the state which is visible to an external observer and therefore states are " hidden" to the outside; hence the name Hidden Markov Model. In isolated speech recognition, it is assumed that the sequence of observed speech vectors corresponding to each word can each be described by a Markov model as follows: O = Oi , 0 2 ) Oτ (1-1) where O. is a speech vector observed at time t. The isolated word recognition then is to compute: arg max { P(w, | O)} (1-2) By using Bayes' Rule, { P(w, | O)} = [P(O I w.) P( Wl )] / P(0) (1-3) In the general case, the Markov model when applied to speech also assumes a finite state machine which changes state once every time unit and each time that a state j is entered, a speech vector 0. is generated from the probability density b, (Ot). Furthermore, the transition from state i to state j is also probabilistic and is governed by the discrete probability a,, For a state sequence X, the joint probability that O is generated by the model M moving through a state sequence X is the product of the transition probabilities and the output probabilities. Only the observation sequence is known — the state sequence is hidden as mentioned before. Given that X is unknown, the required likelihood is computed by summing over all possible state sequences X = x(l), x(2), x(3) , x(T), that is P(O I M) - Σ { a x( o) χ(i)II b(x) (O t ) a x(t) x (t +i) } Given a set of models Mi, corresponding to words w, equation 1 -2 is solved by using 1 -3 and also by assuming that: P(O | w,) = P(O | M.) All of this assumes that the parameters {a,,} and {b,(o t )} are known for each model M,. This can be done, as explained earlier, by using a set of training examples corresponding to a particular model. Thereafter, the parameters of that model can be determined automatically by a robust and efficient re-estimation procedure. So if a sufficient number of representative examples of each word are collected, then a HMM can be constructed which simply models all of the many sources of variability inherent in real speech. This training is well-known in the art, so it is not described at length herein, except to note that the distributed architecture of the present invention enhances the quality of HMMs, since they are derived and constituted at the server side, rather than the client side. In this way, appropriate samples from users of different geographical areas can be easily compiled and analyzed to optimize the possible variations expected to be seen across a particular language to be recognized. Uniformity of the speech recognition process is also well-maintained, and error diagnostics are simplified, since each prospective user is using the same set of HMMs during the recognition process. To determine the parameters of a HMM from a set of training samples, the first step typically is to make a rough guess as to what they might be. Then a refinement is done using the Baum-Welch estimation formulae. By these formulae, the maximum likelihood estimates of μ, (where μ, is mean vector and Σ, is covariance matrix ) is: μ, = ∑ τ t =ι L, (t) o, / [∑ τ t=1 ^ ( t) 0t] A forward-backward algorithm is next used to calculate the probability of state occupation L,(t). If the forward probability CC, (t) for some model M with N states is defined as: α, (t) = P(o_, , o,, x(t) = j | M) This probability can be calculated using the recursion: α, (t) = |∑ N -ι l=2 α(t-l) a„]b, (o.) Similarly the backward probability can be computed using the recursion: β, (t) = a 1 , b,(o t+ ι)(t+l) Realizing that the forward probability is a joint probability and the backward probability is a conditional probability, the probability of state occupation is the product of the two probabilities: j (t) β, (t) = P(O, χ(t) = j | M) Hence the probability of being in state j at a time t is: L,(t) = 1/P [α, (t) β l (t)] where P = P(O | M) To generalize the above for continuous speech recognition, we assume the maximum likelihood state sequence where the summation is replaced by a maximum operation. Thus for a given model M, let φj (t) represent the maximum likelihood of observing speech vectors Oi to O. and being used in state j at time t: φ, (t) = max{φj (t)(t - l) ,} β ) (0t) Expressing this logarithmically to avoid underflow, this likelihood becomes: ψ, (t) = max {ψ, (t - 1) + log( ,)} + log(b,(O t ) This is also known as the Viterbi algorithm. It can be visualized as finding the best path through a matrix where the vertical dimension represents the states of the HMM and horizontal dimension represents frames of speech i.e. time. To complete the extension to connected speech recognition, it is further assumed that each HMM representing the underlying sequence is connected. Thus the training data for continuous speech recognition should consist of connected utterances; however, the boundaries between words do not have to be known. To improve computational speed/efficiency, the Viterbi algorithm is sometimes extended to achieve convergence by using what is known as a Token Passing Model. The token passing model represents a partial match between the observation sequence Oi to Ot and a particular model, subject to the constraint that the model is in state j at time t. This token passing model can be extended easily to connected speech environments as well if we allow the sequence of HMMs to be defined as a finite state network. A composite network that includes both phoneme-based HMMs and complete words can be constructed so that a single-best word can be recognized to form connected speech using word N-best extraction from the lattice of possibilities. This composite form of HMM-based connected speech recognizer is the basis of the NLQS speech recognizer module. Nonetheless, the present invention is not limited as such to such specific forms of speech recognizers, and can employ other techniques for speech recognition if they are otherwise compatible with the present architecture and meet necessary performance criteria for accuracy and speed to provide a real-time dialog experience for users. The representation of speech for the present invention's HMM-based speech recognition system assumes that speech is essentially either a quasi-periodic pulse train (for voiced speech sounds) or a random noise source (for unvoiced sounds). It may be modelled as two sources — one a impulse train generator with pitch period P and a random noise generator which is controlled by a voice/unvoiced switch. The output of the switch is then fed into a gain function estimated from the speech signal and scaled to feed a digital filter H(z) controlled by the vocal tract parameter characteristics of the speech being produced. All of the parameters for this model — the voiced/unvoiced switching, the pitch period for voiced sounds, the gain parameter for the speech signal and the coefficient of the digital filter, vary slowly with time. In extracting the acoustic parameters from the user's speech input so that it can evaluated in light of a set of HMMs, cepstral analysis is typically used to separate the vocal tract information from the excitation information. The cepstrum of a signal is computed by taking the Fourier (or similar) transform of the log spectrum. The principal advantage of extracting cepstral coefficients is that they are de-correlated and the diagonal covariances can be used with HMMs. Since the human ear resolves frequencies non-linearly across the audio spectrum, it has been shown that a front-end that operates in a similar non-linear way improves speech recognition performance. Accordingly, instead of a typical linear prediction-based analysis, the front-end of the NLQS speech recognition engine implements a simple, fast Fourier transform based filter bank designed to give approximately equal resolution on the Mel-scale. To implement this filter bank, a window of speech data (for a particular time frame) is transformed using a software based Fourier transform and the magnitude taken. Each FFT magnitude is then multiplied by the corresponding filter gain and the results accumulated. The cepstral coefficients that are derived from this filter-bank analysis at the front end are calculated during a first partial processing phase of the speech signal by using a Discrete Cosine Transform of the log filter bank amplitudes. These cepstral coefficients are called Mel-Frequency Cepstral Coefficients (MFCC) and they represent some of the speech parameters transferred from the client side to characterize the acoustic features of the user's speech signal. These parameters are chosen for a number of reasons, including the fact that they can be quickly and consistently derived even across systems of disparate capabilities (i.e., for everything from a low power PDA to a high powered desktop system), they give good discrimination, they lend themselves to a number of useful recognition related manipulations, and they are relatively small and compact in size so that they can be transported rapidly across even a relatively narrow band link. Thus, these parameters represent the least amount of information that can be used by a subsequent server side system to adequately and quickly complete the recognition process. To augment the speech parameters an energy term in the form of the logarithm of the signal energy is added. Accordingly, RMS energy is added to the 12 MFCCs to make 13 coefficients. These coefficients together make up the partially processed speech data transmitted in compressed form from the user's client system to the remote server side. The performance of the present speech recognition system is enhanced significantly by computing and adding time derivatives to the basic static MFCC parameters at the server side These two other sets of coefficients — the delta and acceleration coefficients representing change in each of the 13 values from frame to frame (actually measured across several frames), are computed during a second partial speech signal processing phase to complete the initial processing of the speech signal, and are added to the original set of coefficients after the latter are received These MFCCs together with the delta and acceleration coefficients constitute the observation vector Ot mentioned above that is used for determining the appropriate HMM for the speech data The delta and acceleration coefficients are computed using the following regression formula where d t is a delta coefficient at time t computed in terms of the corresponding static coefficients In a typical stand-alone implementation of a speech recognition system, the entire SR engine runs on a single client In other words, both the first and second partial processing phases above are executed by the same DSP (or microprocessor) running a ROM or software code routine at the client's computing machine. In contrast, because of several considerations, specifically - cost, technical performance, and client hardware uniformity, the present NLQS system uses a partitioned or distributed approach While some processing occurs on the client side, the main speech recognition engine runs on a centrally located server or number of servers More specifically, as noted earlier, capture of the speech signals, MFCC vector extraction and compression are implemented on the client's machine during a first partial processing phase The routine is thus streamlined and simple enough to be implemented within a browser program (as a plug in module, or a downloadable applet for example) for maximum ease of use and utility Accordingly, even very "thin" client platforms can be supported, which enables the use of the present system across a greater number of potential sites The primary MFCCs are then transmitted to the server over the channel, which, for example, can include a dial-up Internet connection, a LAN connection, a wireless connection and the like After decompression, the delta and acceleration coefficients are computed at the server to complete the initial speech processing phase, and the resulting observation vectors O t are also determined. General Aspects of Speech Recognition Engine The speech recognition engine is also located on the server, and is based on a HTK-based recognition network compiled from a word-level network, a dictionary and a set of HMMs. The recognition network consists of a set of nodes connected by arcs. Each node is either a HMM model instance or a word end. Each model node is itself a network consisting of states connected by arcs. Thus when fully compiled, a speech recognition network consists of HMM states connected by transitions. For an unknown input utterance with T frames, every path from the start node to the exit node of the network passes through T HMM states. Each of these paths has log probability which is computed by summing the log probability of each individual transition in the path and th elog probability of each emitting state generating the corresponding observation. The function of the Viterbi decoder is find those paths throuh the network which have the highest log probability. This is found uisng the Token Passing algorithm. In a network that has many nodes, the computation time is reduced by only allowing propagation of those tokens which will have some chance of becoming winners. This process is called pruning. Natural Language Processor In a typical natural language interface to a database, the user enters a question in his/her natural language, for example, English. The system parses it and translates it to a query language expression. The system then uses the query language expression to process the query and if the search is successful, a recordset representing the results is displayed in English either formatted as raw text or in a graphical form. For a natural langauge interface to work well involves a number of technical requirements. For example, it needs to be robust - in the sentence 'What's the departments turnover' it needs to decide that the word whats — what's — what is. And it also has to determine that departments — department's. In addition to being robust, the natural language interface has to distinguish between the several possible forms of ambiguity that may exist in the natural language — lexical, structural, reference and ellipsis ambiguity. All of these requirements, in addition to the general ability to perform basic linguistic morphological operations of tokenization, tagging and grouping, are implemented within the present invention. Tokenization is implemented by a text analyzer which treats the text as a series of tokens or useful meaningful units that are larger than individual characters, but smaller than phrases and sentences. These include words, separable parts of words, and punctuation. Each token is associated with an offset and a length. The first phase of tokenization is the process of segmentation which extracts the individual tokens from the input text and keeps track of the offset where each token originated in the input text. The tokenizer ouput lists the offset and category for each token. In the next phase of the text analysis, the tagger uses a built-in morphological analyzer to look up each word/token in a phrase or sentence and interanlly lists all parts of speech. The output is the input string with each token tagged with a parts of speech notation. Finally the grouper which functions as a phrase extractor or phrase analyzer, determines which groups of words form phrases. These three operations which are the foundations for any modern linguistic processing schemes, are fully implemented in optimized algorithms for determining the single-best possible answer to the user's question. SQL Database and Full-Text Query Another key component of present system is a SQL-database. This database is used to store text, specifically the the answer-question pairs are stored in full-text tables of the database. Additionally, the full-text search capability of the database allows full- text searches to be carried out. While a large portion of all digitally stored information is in the form of unstructured data, primarily text, it is now possible to store this textual data in traditional database systems in character-based columns such as varchar and text. In order to effectively retrieve textual data from the database, techniques have to be implemented to issue queries against textual data and to retrieve the answers in a meaningful way where it provides the answers as in the case of the NLQS system. There are two major types of textual searches: Property - This search technology first applies filters to documents in order to extract properties such as author, subject, typ e > word count, printed page count, and time last written, and then issues searches against those properties; Full-text —this search technology first creates indexes of all non- noise words in the documents, and then uses these indexes to support linguistic searches and proximity searches. Two additional technologies are also implemented in this particular RDBMs: SQL Server also have been integrated: A Search service - a full-text indexing and search service that is called both index engine and search, and a parser that accepts full-text SQL extensions and maps them into a form that can be processed by the search engine. The four ma j or aspects involved in implementing full-text retrieval of plain-text data from a full-text-capable database are: Managing the definition of the tables and columns that are registered for full-text searches; Indexing the data in registered columns - the indexing process scans the character streams, determines the word boundaries (this is called word breaking), removes all noise words (this also is called stop words), and then populates a full-text index with the remaining words, Issuing queries against registered columns for populated full-text indexes; Ensuring that subsequent changes to the data in registered columns gets propagated to the index engine to keep the full-text indexes synchronized The underlying design principle for the indexing, querying, and synchronizing processes is the presence of a full-text unique key column (or single-column primary key) on all tables registered for full-text searches. The full-text index contains an entry for the non-noise words in each row together with the value of the key column for each row. When processing a full-text search, the search engine returns to the database the key values of the rows that match the search criteria. The full-text administration process starts by designating a table and its columns of interest for full-text search. Customized NLQS stored procedures are used first to register tables and columns as eligible for full-text search After that, a separate request by means of a stored procedure is issued to populate the full-text indexes. The result is that the underlying index engine gets invoked and asynchronous index population begins Full-text indexing tracks which significant words are used and where they are located. For example, a full-text index might indicate that the word "NLQS" is found at word number 423 and word number 982 in the Abstract column of the DevTools table for the row associated with a ProductID of 6. This index structure supports an efficient search for all items containing indexed words as well as advanced search operations, such as phrase searches and proximity searches. (An example of a phrase search is looking for "white elephant'' where "white" is followed by "elephant' . An example of a proximity search is looking for "big" and "house" where "big" occurs near "house") To prevent the full-text index from becoming bloated, noise words such as "a," "and," and "the" are ignored. Extensions to the Transact-SQL language are used to construct full-text queries. The two key predicates that are used in the NLQS are CONTAINS and FREETEXT. The CONTAINS predicate is used to determine whether or not values in full- text registered columns contain certain words and phrases. Specifically, this predicate is used to search for: • A word or phrase. • The prefix of a word or phrase. • A word or phrase that is near another. • A word that is an inflectional form of another (for example, "dήve" is the inflectional stem of "drives," "drove," "driving," and "driven"). • A set of words or phrases, each of which is assigned a different weighting. The relational engine within SQL Server recognizes the CONTAINS and FREETEXT predicates and performs some minimal syntax and semantic checking, such as ensuring that the column referenced in the predicate has been registered for full- text searches. During query execution, a full-text predicate and other relevant information are passed to the full-text search component. After further syntax and semantic validation, the search engine is invoked and returns the set of unique key values identifying those rows in the table that satisfy the full-text search condition. In addition to the FREETEXT and CONTAINS, other predicates such as AND, LIKE, NEAR are combined to create the customized NLQS SQL construct. Full-Text Query Architecture of the SQL Database The full-text query architecture is comprised of the following several components - Full-Text Query component, the SQL Server Relational Engine, the Full-Text provider and the Search Engine. The Full-Text Query component of the SQL database accept a full-text predicate or rowset-valued function from the SQL Server; transform parts of the predicate into an internal format, and sends it to Search Service, which returns the matches in a rowset. The rowset is then sent back to SQL Server. SQL Server uses this information to create the resultset that is then returned to the submitter of the query. The SQL Server Relational Engine accepts the CONTAINS and FREETEXT predicates as well as the CONTAINSTABLE() and FREETEXTTABLE() rowset-valued functions. During parse time, this code checks for conditions such as attempting to query a column that has not been registered for full- text search. If valid, then at run time, the ft_search_condition and context information is sent to the full-text provider. Eventually, the full-text provider returns a rowset to SQL Server, which is used in any joins (specified or implied) in the original query. The Full- Text Provider parses and validates the ft_search_condition, constructs the appropriate internal representation of the full-text search condition, and then passes it to the search engine. The result is returned to the relational engine by means of a rowset of rows that satisfy ft_search_condition. Client Side System 150 The architecture of client-side system 150 of Natural Language Query System 100 is illustrated in greater detail in FIG. 2. Referring to FIG. 2, the three main processes effectuated by Client System 150 are illustrated as follows: Initialization process 200A consisting of SRE 201, Communication 202 and MS Agent 203 routines; an iterative process 200B consisting of two sub-routines: a) Receive User Speech 208 — made up of SRE 204 and Communication 205; and b) Receive Answer from Server 207 and an un- initialization process 200B. Finally, un-initialization process 200C is made up of three sub-routines: SRE 212, Communication 213, and MS Agent 214. Each of the above three processes are described in detail in the following paragraphs. It will be appreciated by those skilled in the art that the particular implementation for such processes and routines will vary from client platform to platform, so that in some environments such processes may be embodied in hard-coded routines executed by a dedicated DSP, while in others they may be embodied as software routines executed by a shared host processor, and in still others a combination of the two may be used. Initialization at Client System 150 The initialization of the Client System 150 is illustrated in FIG. 2-2 and is comprised generally of 3 separate initializing processes: client-side Speech Recognition Engine 220A, MS Agent 220B and Communication processes 220C. Initialization of Speech Recognition Engine 220A Speech Recognition Engine 155 is initialized and configured using the routines shown in 220A. First, an SRE COM Library is initialized. Next, memory 220 is allocated to hold Source and Coder objects, are created by a routine 221. . Loading of configuration file 221A from configuration data file 221B also takes place at the same time that the SRE Library is initialized. In configuration file 221B, the type of the input of Coder and the type of the output of the Coder are declared. The structure, operation, etc. of such routines are weD-known in the art, and they can be implemented using a number of fairly straightforward approaches. Accordingly, they are not discussed in detail herein. Next, Speech and Silence components of an utterance are calibrated using a routine 222, in a procedure that is also well-known in the art. To calibrate the speech and silence components, the user preferably articulates a sentence that is displayed in a text box on the screen. The SRE library then estimates the noise and other parameters required to find e silence and speech elements of future user utterances. Initialization of MS Agent 220B The software code used to initialize and set up a MS Agent 220B is also illustrated in FIG. 2-2. The MS Agent 220B routine is responsible for coordinating and handling the actions of the animated agent 157 (FIG.l). This initialization thus consists of the following steps: 1. Initialize COM library 223. This part of the code initializes the COM library, which is required to use ActiveX Controls, which controls are well-known in the art. 2. Create instance of Agent Server 224 - this part of the code creates an instance of Agent ActiveX control. 3. Loading of MS Agent 225 - this part of the code loads MS Agent character from aspecified file 225A containing general parameter data for the Agent Character, such as the overall appearance, shape, size, etc.. 4. Get Character Interface 226 - this part of the code gets an appropriate interface for the specified character; for example, characters may have different control/interaction capabilities that can be presented to the user. 5. Add Commands to Agent Character Option 227 - this part of the code adds commands to an Agent Properties sheet, which sheet can be accessed by clicking on the icon that appears in the system tray, when the Agent character is loaded e.g. that the character can Speak, how he/she moves, TTS Properties, etc. 6. Show the Agent Character 228 - this part of the code displays the Agent character on the screen so it can be seen by the user; 7. AgentNoufySink - to handle events. This part of the code creates AgentNotifySink object 229, registers it at 230 and then gets the Agent Properties interface 231. The property sheet forthe Agent character is assigned using routine 232. 8. Do Character Animations 233 - This part of the code plays specified character animations to welcome the user to NLQS 100 The above then constitutes the entire sequence required to initialize the MS Agent. As with the SRE routines, the MS Agent routines can be implemented in any suitable and conventional fashion by those skilled in the art based on the present teachings. The particular structure, operation, etc of such routines is not critical, and thus they are not discussed in detail herein In a preferred embodiment, the MS Agent is configured to have an appearance and capabilities that are appropriate for the particular application For instance, in a remote learning application, the agent has the visual form and mannerisms/attitude/gestures of a college professor Other visual props (blackboard, textbook, etc ) may be used by the agent and presented to the user to bring to mind the experience of being in an actual educational environment The characteristics of the agent may be configured at the client side 150, and/or as part of code executed by a browser program (not shown) in response to configuration data and commands from a particular web page. For example, a particular website offering medical services may prefer to use a visual image of a doctor These and many other variations will be apparent to those skilled in the art for enhancing the human-like, real-time dialog experience for users. Initialization of Communication Link 160A The initialization of Communication Link 160A is shown with reference to process 220C FIG. 2-2. Referring to FIG. 2-2, this initialization consists of the following code components Open Internet Connection 234 - this part of the code opens an Internet Connection and sets the parameter for the connection Then Set Callback Status routine 235 sets the callback status so as to inform the user of the status of connection. Finally Start New HTTP Internet Session 236 starts a new Internet session. The details of Communications Link 160 and the set up process 220C are not critical, and will vary from platform to platform. Again, in some cases, users may use a low-speed dial-up connection, a dedicated high speed switched connection (Tl for example), an always-on xDSL connection, a wireless connection, and the like. Iterative Processing of Queries /Answers As illustrated in FIG. 3, once initialization is complete, an iterative query/answer process is launched when the user presses the Start Button to initiate a query. Referring to FIG. 3, the iterative query/answer process consists of two main sub-processes implemented as routines on the client side system 150. Receive User Speech 240 and Receive User Answer 243. The Receive User Speech 240 routine receives speech from the user (or another audio input source), while the Receive User Answer 243 routine receives an answer to the user's question in the form of text from the server so that it can be converted to speech for the user by text-to-speech engine 159. As used herein, the term "query" is referred to in the broadest sense to refer, to either a question, a command, or some form of input used as a control variable by the system. For example, a query may consist of a question directed to a particular topic, such as "what is a network" in the context of a remote learning application. In an e-commerce application a query might consist of a command to "list all books by Mark Twain" for example Similarly, while the answer in a remote learning application consists of text that is rendered into audible form by the text to speech engine 159, it could also be returned as another form of multi-media information, such as a graphic image, a sound file, a video file, etc depending on the requirements of the particular application Again, given the present teachings concerning the necessary structure, operation, functions, performance, etc , of the client-side Receive User Speech 240 and Receiver User Answer 243 routines, one of ordinary skill in the art could implement such in a variety of ways. Receive User Speech — As illustrated in FIG. 3, the Receive User Speech routine 240 consists of a SRE 241 and aCommunication 242 process, both implemented again as routines on the client side system 150 for receiving and partially processing the user's utterance SRE routine 241 uses a coder 248 which is prepared so that a coder object receives speech data from asource object Next the Start Source 249 routine is initiated This part of the code initiates data retrieval using the source Object which will in turn be given to the Coder object. Next, MFCC vectors 250 are extracted from the Speech utterance continuously until silence is detected As alluded to earlier, this represents the first phase of processing of the input speech signal, and in a preferred embodiment, it is intentionally restricted to merely computing the MFCC vectors for the reasons already expressed above. These vectors include the 12 cepstral coefficients and the RMS energy term, for a total of 13 separate numerical values for the partially processed speech signal. In some environments, nonetheless, it is conceivable that the MFCC delta parameters and MFCC acceleration parameters can also be computed at client side system 150, depending on the computation resources available, the transmission bandwidth in data link 160A available to server side system 180, the speed of a transceiver used for carrying data in the data link, etc These parameters can be determined automatically by client side system upon initializing SRE 155 (using some type of calibration routine to measure resources), or by direct user control, so that the partitioning of signal processing responsibilities can be optimized on a case-by-case basis In some applications, too, server side system 180 may lack the appropriate resources or routines for completing the processing of the speech input signal Therefore, for some applications, the allocation of signal processing responsibilities may be partitioned differendy, to the point where in fact both phases of the speech signal processing may take place at client side system 150 so that the speech signal is completely - rather than partially —processed and transmitted for conversion into a query at server side system 180 Again in a preferred embodiment, to ensure reasonable accuracy and real-time performance from a query/response perspective, sufficient resources are made available in a client side system so that 100 frames per second of speech data can be partially processed and transmitted through link 160A Since the least amount of information that is necessary to complete the speech recognition process (only 13 coefficients) is sent, the system achieves a real-time performance that is believed to be highly optimized, because other latencies (l e , client-side computational latencies, packet formation latencies, transmission latencies) are minimized It will be apparent that the principles of the present invention can be extended to other SR applications where some other methodology is used for breaking down the speech input signal by an SRE (i.e., non- MFCC based) The only criteria is that the SR processing be similarly dividable into multiple phases, and with the responsibility for different phases being handled on opposite sides of link 160A depending on overall system performance goals, requirements and the like This functionality of the present invention can thus be achieved on a system by system basis, with an expected and typical amount of optimization being necessary for each particular implementation Thus, the present invention achieves a response rate performance that is tailored in accordance with the amount of information that is computed, coded and transmitted by the client side system 150 So in applications where real-time performance is most critical, the least possible amount of extracted speech data is transmitted to reduce these latencies, and, in other applications, the amount of extracted speech data that is processed, coded and transmitted can be varied Communication — transmit communication module 242 is used to implement the transport of data from the client to the server over the data link 160A, which in a preferred embodiment is the Internet. As explained above, the data consists of encoded MFCC vectors that will be used at then server-side of the Speech Recognition engine to complete the speech recognition decoding. The sequence of the communication is as follows OpenHTTPRequest 251 - this part of the code first converts MFCC vectors to a stream of bytes, and then processes the bytes so that it is compatible with a protocol known as HTTP This protocol is well-known in the art, and it is apparent that for other data links another suitable protocol would be used 1 Encode MFCC Byte Stream 251 - this part of the code encodes the MFCC vectors, so that they can be sent to the server via HTTP. 2. Send data 252 - this part of the code sends MFCC vectors to the server using the Internet connection and the HTTP protocol. Wait for the Server Response 253 - this part of the code monitors the data link 160A a response from server side system 180 arrives. In summary, the MFCC parameters are extracted or observed on-the-fly from the input speech signal. They are then encoded to a HTTP byte stream and sent in a streaming fashion to the server before the silence is detected — l e. sent to server side system 180 before the utterance is complete This aspect of the invention also facilitates a real-time behavior, since data can be transmitted and processed even while the user is still speaking. Receive Answer from Server 243 is comprised of the following modules as shown in FIG. 3 MS Agent 244, Text-to-Speech Engine 245 and receive communication modules 246. All three modules interact to receive the answer from server side system 180 As illustrated in FIG. 3, the receive communication process consists of three separate processes implemented as a receive routine on client side system 150 a Receive the Best Answer 258 receives the best answer over data link 160B (the HTTP communication channel). The answer is de-compressed at 259 and then the answer is passed by code 260 to the MS Agent 244, where it is received by code portion 254 . A routine 255 then articulates the answer using text-to-speech engine 257. Of course, the text can also be displayed for additional feedback purposes on a monitor used with client side system 150. The text to speech engine uses a natural language voice data file 256 associated with it that is appropriate for the particular language application (i.e., English, French, German, Japanese, etc ) As explained earlier when the answer is something more than text, it can be treated as desired to provide responsive information to the user, such as with a graphics image, a sound, a video clip, etc. Uninitialization The un-initialization routines and processes are illustrated in FIG. 4 Three functional modules are used for un-iniualizing the primary components of the client side system 150, these include SRE 270, Communications 271 and MS Agent 272 un- lnitializing routines. To un-initialize SRE 220A, memory that was allocated in the initialization phase is de-allocated by code 273 and objects created during such initialization phase are deleted by code 274. Similarly, as illustrated in FIG. 4, to un- initialize Communications module 220C the Internet connection previously established with the server is closed by code portion 275 of the Communication Un-itialization routine 271 Next the Internet session created at the time of initialization is also closed by routine 276. For the un-initialization of the MS Agent 220B, ,as illustrated in FIG. 4, MS Agent Un-initialization routine 272 first releases the Commands Interface 227 using routine 277 This releases the commands added to the property sheet during loading of the agent character by routine 225. Next the Character Interface initialized by routine 226 is released by routine 278 and the Agent is unloaded at 279. The Sink Object Interface is then also released 280 followed by the release of the Property Sheet Interface 281. The Agent Notify Sink 282 then un-registers the Agent and finally the Agent Interface 283 is released which releases all the resources allocated during initialization steps identified in FIG. 2-2. It will be appreciated by those skilled in the art that the particular implementation for such un-mitialization processes and routines in FIG 4 will vary from client platform to client platform, as for the other routines discussed above. The structure, operation, etc. of such routines are well-known in the art, and they can be implemented using a number of fairly straightforward approaches without undue effort. Accordingly, they are not discussed in detail herein DESCRIPTION OF SERVER SIDE SYSTEM 180 Introduction A high level flow diagram of the set of preferred processes implemented on server side system 180 of Natural Language Query System 100 is illustrated in Figs. 11A through Fig. 11C. In a preferred embodiment, this process consists of a two step algorithm for completing the processing of the speech input signal, recognizing the meaning of the user's query, and retrieving an appropriate answer/response for such query The 1 st step as illustrated in Fig. 11A can be considered a high-speed first-cut pruning mechanism, and includes the following operations: after completing processing of the speech input signal, the user's query is recognized at step 1101, so that the text of the query is simultaneously sent to Natural Language Engine 190 (FIG. 1) at step 1107, and to DB Engine 186 (also FIG.l) at step 1102. By "recognized" in this context it is meant that the user's query is converted into a text string of distinct native language words through the HMM technique discussed earlier At NLE 190, the text string undergoes morphological linguistic processing at step 1108: the string is tokenizedthe tags are tagged and the tagged tokens are grouped Next the noun phrases (NP) of the string are stored at 1109, and also copied and transferred for use by DB Engine 186 during a DB Process at step 1110. As illustrated in Fig. 11A, the string corresponding to the user's query which was sent to the DB Engine 186 at 1102, is used together with the NP received from NLE 190 to construct an SQL Query at step 1103. Next, the SQL query is executed at step 1104, and a record set of potential questions corresponding to the user's query are received as a result of a full-text search at 1105, which are then sent back to NLE 190 in the form of an array at step 1106. As can be seen from the above, this first step on the server side processing acts as an efficient and fast pruning mechanism so that the universe of potential "hits" corresponding to the user's actual query is narrowed down very quickly to a manageable set of likely candidates in a very short period of time Referring to Fig. 11B, in contrast to the first step above, the 2 nd step can be considered as the more precise selection portion of the recognition process. It begins with linguistic processing of each of the stored questions in the array returned by the full- text search process as possible candidates representing the user's query. Processing of these stored questions continues in NLE 190 as follows- each question in the array of questions corresponding to the record set returned by the SQL full-text search undergoes morphological linguistic processing at step llll in this operation, a text string corresponding to the retrieved candidate question is tokenized, the tags are tagged and the tagged tokens are grouped Next, noun phrases of the string are computed and stored at step 1112. This process continues iteratively at point 1113, and the sequence of steps at 1118,1111, 1112, 1113 are repeated so that an NP for each retrieved candidate question is computed and stored Once an NP is computed for each of the retrieved candidate questions of the array, a comparison is made between each such retrieved candidate question and the user's query based on the magnitude of the NP value at step 1114 This process is also iterative in that steps 1114, 1115, 1116, 1119 are repeated so that the comparison of the NP for each retrieved candidate question with that of the NP of the user's query is completed When there are no more stored questions in the array to be processed at step 1117, the stored question that has the maximum NP relative to the user's query, is identified at 1117A as the stored question which best matches the user's query Notably, it can be seen that the second step of the recognition process is much more computationally intensive than the first step above, because several text strings are tokenized, and a comparison is made of several NPs This would not be practical, nonetheless, if it were not for the fact that the first step has already quickly and efficiently reduced the candidates to be evaluated to a significant degree Thus, this more computationally intensive aspect of the present invention is extremely valuable, however because it yields extremely high accuracy in the overall query recognition process In this regard, therefore, this second step of the query recognition helps to ensure the overall accuracy of the system, while the first step helps to maintain a satisfactory speed that provides a real-time feel for the user As illustrated in Fig. 11C, the last part of the query/response process occurs by providing an appropriate matching answer/response to the user Thus, an identity of a matching stored question is completed at step 1120 Next a file path corresponding to an answer of the identified matching question is extracted at step 1121 Processing continues so that the answer is extracted from the file path at 1122 and finally the answer is compressed and sent to client side system 150 at step 1123. The discussion above is intended to convey a general overview of the primary components, operations, functions and characteristics of those portions of NLQS system 100 that reside on server side system 180. The discussion that follows describes in more detail the respective sub-systems. Software modules used in Server Side System 180 The key software modules used on server-side system 180 of the NLQS system are illustrated in Fig. 5 These include generally the following components: a Communication module 500 — identified as CommumcationServer ISAPI 500A (which is executed by SRE Server-side 182 — FIG. 1 and is explained in more detail below), and a database process DBProcess module 501 (executed by DB Engine 186 — FIG. 1). Natural language engine module 500C (executed by NLE 190 - FIG.l) and an interface 500B between the NLE process module 500C and the DBProcess module 500B. As shown here, CommunicationServerlSAPI 500A includes a server-side speech recognition engine and appropriate communication interfaces required between client side system 150 and server side system 180 As further illustrated in Fig. 5, server-side logic of Natural Language Query System 100 also can be characterized as including two dynamic link library components CommunicationServerlSAPI 500 and DBProcess 501. The CommunicationServerlASPI 500 is comprised of 3 sub-modules: Server-side Speech Recognition Engine module 500A, Interface module 500B between Natural Language Engine modules 500C and DBProcess 501 , and the Natural Language Engine modules 500C. DB Process 501 is a module whose primary function is to connect to a SQL database and to execute an SQL query that is composed in response to the user's query In addition, this module interfaces with logic that fetches the correct answer from a file path once this answer is passed to it from the Natural Language Engine module 500C Speech Recognition Sub-System 182 on Server-Side System 180 The server side speech recognition engine module 500A is a set of distributed components that perform the necessary functions and operations of speech recognition engine 182 (FIG 1) at server-side 180 These components can be implemented as software routines that are executed by server side 180 in conventional fashion. Referring to Fig. 4A, a more detailed break out of the operation of the speech recognition components at the server-side can be seen as follows: Within a portion 601 of the server side SRE module 500A, the binary MFCC vector byte stream corresponding to the speech signal's acoustic features extracted at client side system 150 and sent over the communication channel 160 is received. The MFCC acoustic vectors are decoded from the encoded HTTP byte stream as follows Since the MFCC vectors contain embedded NULL characters, they cannot be transferred in this form to server side system 180 as such using HTTP protocol Thus the MFCC vectors are first encoded at client-side 150 before transmission in such a way that all the speech data is converted into a stream of bytes without embedded NULL characters in the data At the very end of the byte stream a single NULL character is introduced to indicate the termination of the stream of bytes to be transferred to the server over the Internet 160A using HTTP protocol As explained earlier, to conserve latency time between the client and server, a smaller number of bytes (just the 13 MFCC coefficients) are sent from client side system 150 to server side system 180 This is done automatically for each platform to ensure uniformity, or can be tailored by the particular application environment — l e , such as where it is determined that it will take less time to compute the delta and acceleration coefficients at the server (26 more calculations), than it would take to encode them at the client, transmit them, and then decode them from the HTTP stream In general, since server side system 180 is usually better equipped to calculate the MFCC delta and acceleration parameters, this is a preferable choice Furthermore, there is generally more control over server resources compared to the client's resources, which means that future upgrades, optimizations, etc , can be disseminated and shared by all to make overall system performance more reliable and predictable So, the present invention can accommodate even the worst-case scenario where the client's machine may be quite thin and may just have enough resources to capture the speech input data and do minimal processing Dictionary Preparation & Grammar Files Referring to Fig. 4A, within code block 605, various options selected by the user (or gleaned from the user's status within a particular application) are received For instance, in the case of a preferred remote learning system, Course, Chapter and/or Section data items are communicated In the case of other applications (such as e- commerce) other data options are communicated, such as the Product Class, Product Category, Product Brand, etc loaded for viewing within his/her browser These selected options are based on the context experienced by the user during an interactive process, and thus help to limit and define the scope - l e grammars and dictionaries that will be dynamically loaded to speech recognition engine 182 (FIG 1) for Viterbi decoding during processing of the user speech utterance For speech recognition to be optimized both grammar and dictionary files are used in a preferred embodiment. A Grammar file supplies the universe of available user queries, 1 e , all the possible words that are to be recognized The Dictionary file provides phonemes (the information of how a word is pronounced — this depends on the specific native language files that are installed — for example, UK English or US English) of each word contained in the grammar file It is apparent that if all the sentences for a given environment that can be recognized were contained in a single grammar file then recognition accuracy would be deteriorated and the loading time alone for such grammar and dictionary files would impair the speed of the speech recognition process To avoid these problems, specific grammars are dynamically loaded or actively configured as the current grammar according to the user's context, l e , as in the case of a remote learning system, the Course, Chapter and/or Section selected Thus the grammar and dictionary files are loaded dynamically according to the given Course, Chapter and/or Section as dictated by the user, or as determined automatically by an application program executed by the user The second code block 602 implements the initialization of Speech Recognition engine 182 (FIG 1) The MFCC vectors received from client side system 150 along with the grammar filename and the dictionary file names are introduced to this block to initialize the speech decoder As illustrated in Fig 4A, the initialization process 602 uses the following subroutines A routine 602a for loading an SRE library. This then allows the creation of an object identified as External Source with code 602b using the received MFCC vectors Code 602c allocates memory to hold the recognition objects. Routine 602d then also creates and initializes objects that are required for the recognition such as Source, Coder, Recognizer and Results Loading of the Dictionary created by code 602e, Hidden Markov Models (HMMs) generated with code 602f, and Loading of the Grammar file generated by routine 602g Speech Recognition 603 is the next routine invoked as illustrated in Fig. 4A , and is generally responsible for completing the processing of the user speech signals input on the client side 150, which, as mentioned above, are preferably only partially processed (l e , only MFCC vectors are computed during the first phase) when they are transmitted across link 160 Using the functions created in External Source by subroutine 602b, this code reads MFCC vectors, one at a time from an External Source 603a, and processes them in block 603b to realize the words in the speech pattern that are symbolized by the MFCC vectors captured at the client. During this second phase, an additional 13 delta coefficients and an additional 13 acceleration coefficients are computed as part of the recognition process to obtain a total of 39 observation vectors O t referred to earlier Then, using a set of previously defined Hidden Markov Models (HMMs), the words corresponding to the user's speech utterance are determined in the manner described earlier This completes the word "recognition" aspect of the query processing, which results are used further below to complete the query processing operations It will be appreciated by those skilled in the art that the distributed nature and rapid performance of the word recognition process, by itself, is extremely useful and may be implemented in connection with other environments that do not implicate or require additional query processing operations For example, some applications may simply use individual recognized words for filling in data items on a computer generated form, and the aforementioned systems and processes can provide a rapid, reliable mechanism for doing so Once the user's speech is recognized, the flow of SRE 182 passes to Un-initialize SRE routine 604 where the speech engine is un-initialized as illustrated In this block all the objects created in the initialization block are deleted by routine 604a, and memory allocated in the initialization block during the initialization phase are removed by routine 604b Again, it should be emphasized that the above are merely illustrative of embodiments for implementing the particular routines used on a server side speech recognition system of the present invention. Other variations of the same that achieve the desired functionality and ob j ectives of the present invention will be apparent from the present teachings. Database Processor 186 Operation - DBProcess Construction of an SQL Query used as part of the user query processing is illustrated in Fig. 4B, a SELECT SQL statement is preferably constructed using a conventional CONTAINS predicate. Module 950 constructs the SQL query based on this SELECT SQL statement, which query is used for retrieving the best suitable question stored in the database corresponding to the user's articulated query, (designated as Question here) A routine 951 then concatenates a table name with the constructed SELECT statement Next, the number of words present in each Noun Phrase of Question asked by the user is calculated by routine 952 Then memory is allocated by routine 953 as needed to accommodate all the words present in the NP Next a word List (identifying all the distinct words present in the NP) is obtained by routine 954. After this, this set of distinct words are concatenated by routine 955 to the SQL Query separated with a NEAR ( ) keyword. Next, the AND keyword is concatenated to the SQL Query by routine 956 after each NP. Finally memory resources are freed by code 957 so as to allocate memory to store the words received from NP for any next iteration. Thus, at the end of this process, a completed SQL Query corresponding to the user's articulated question is generated. Connection to SQL Server - As illustrated in Fig. 4C, after the SQL Query is constructed by routine 710, a routine 711 implements a connection to the query database 717 to continue processing of the user query. The connection sequence and the subsequent retrieved record set is implemented using the following routines: 1. Server and database names are assigned by routine 711 A to a DBProcess member variable 2. A connection string is established by routine 71 IB; 3. The SQL Server database is connected under control of code 711C 4. The SQL Query is received by routine 712A 5. The SQL Query is executed by code 712B 6. Extract the total number of records retrieved by the query - 713 7. Allocate the memory to store the total number of paired questions — 713 8. Store the entire number of paired questions into an array — 713 Once the Best Answer ID is received at 716 FIG 4C, from the NLE 14 (FIG 5), the code corresponding 716C receives it passes it to code in 716B where the path of the Answer file is determined using the record number. Then the file is opened 716C using the path passed to it and the contents of the file corresponding to the answer is read. Then the answer is compressed by code in 716D and prepared for transmission over the communication channel 160B (FIG 1). NLQS Database 188 - Table Organization Figure 7 illustrates a preferred embodiment of a logical structure of tables used in a typical NLQS database 188 (FIG.l). When NLQS database 188 is used as part of NLQS query system 100 implemented as a remote learning/ training environment, this database will include an organizational multi-level hierarchy that consists typically of a Course 701, which is made of several chapters 702, 703, 704 Each of these chapters can have one or more Sections 705, 706, 707 as shown for Chapter 1 A similar structure can exist for Chapter 2, Chapter 3 Chapter N Each section has a set of one or more question - answer pairs 708, 709, 710 stored in tables described in more detail below While this is an appropriate and preferable arrangement for a training/learning application, it is apparent that other implementations would be possible and perhaps more suitable for other applications such as e-commerce, e-support, internet browsing, etc , depending on overall system parameters It can be seen that the NLQS database 188 organization is intricately linked to the switched grammar architecture described earlier In other words, the context (or environment) experienced by the user can be determined at any moment in time based at the selection made at the section level, so that only a limited subset of question-answer pairs 708 for example are appropriate for section 705 This in turn means that only a particular appropriate grammar for such question-answer pairs may be switched in for handling user queries while the user is experiencing such context In a similar fashion, an e-commerce application for an internet based business may consist of a hierarchy that includes a first level "home" page 701 identifying user selectable options (product types, services, contact information, etc ), a second level may include one or more "product types" pages 702, 703, 704, a third page may include particular product models 705, 706, 707, etc , and with appropriate question-answer pairs 708, 709, and grammars customized for handling queries for such product models Again, the particular implementation will vary from application to application, depending on the needs and desires of such business, and a typical amount of routine optimization will be necessary for each such application Table Organization In a preferred embodiment, an independent database is used for each Course Each database in turn can include three types of tables as follows a Master Table as illustrated in Figure 7A, at least one Chapter Table as illustrated in Figure 7B and at least one Section Table as illustrated in Figure 7C As illustrated in Fig. 7A, a preferred embodiment of a Master Table has six columns - Field Name 701A, Data Type 702A, Size 703A, Null 704A, Primary Key 705A and Indexed 706A These parameters are well-known in the art of database design and structure The Master Table has only two fields — Chapter Name 707A and Section Name 708A Both ChapterName and Section Name are commonly indexed. A preferred embodiment of a Chapter Table is illustrated in Figure 7B. As with the Master Table, the Chapter Table has six (6) columns - Field Name 720, Data Type 721, Size 722, Null 723, Primary Key 724 and Indexed 725. There are nine (9) rows of data however, in this case, - ChapterJD 726, Answer_ID 727, Section Name 728, Answer_Tιtle 729, PairedQuestion 730, AnswerPath 731, Creator 732, Date of Creation 733 and Date of Modification 734. An explanation of the Chapter Table fields is provided in Figure 7C. Each of the eight (8) Fields 730 has a description 731 and stores data corresponding to- AnswerlD 732 — an integer that is automatically incremented for each answer given for user convenience Section_Name 733 — the name of the section to which the particular record belongs This field along with the AnswerlD is used as the primary key Answer_Tιtle 734 — A short description of the title of the answer to the user query PairedQuestion 735 — Contains one or more combinations of questions for the related answers whose path is stored in the next column AnswerPath AnswerPath 736 — contains the path of a file, which contains the answer to the related questions stored in the previous column; in the case of a pure question/answer application, this file is a text file, but, as mentioned above, could be a multi-media file of any kind transportable over the data link 160 Creator 737— Name of Content Creator Date_of_Creatιon 738 - Date on which content was created Date of Modification 739 - Date on which content was changed or modified A preferred embodiment of a Section Table is illustrated in Figure 7D The Section Table has six (6) columns - Field Name 740, Data Type 741, Size 742, Null 743, Primary Key 744 and Indexed 745 There are seven (7) rows of data — Answer_ID 746, Answer_Tιtle 747, PairedQuestion 748, AnswerPath 749, Creator 750, Date of Creation 751 and Date of Modification 752. These names correspond to the same fields, columns already described above for the Master Table and Chapter Table. Again, this is a preferred approach for the specific type of learning/ training application described herein. Since the number of potential applications for the present invention is quite large, and each application can be customized, it is expected that other appbcations (including other learning/ training appbcations) will require and/or be better accommodated by another table, column, and field structure/hierarchy. Search Service and Search Engine - A query text search service is performed by an SQL Search System 1000 shown in FIG 10 This system provides querying support to process full-text searches This is where full-text indexes reside. In general, SQL Search System determines which entries in a database index meet selection criteria specified by a particular text query that is constructed in accordance with an articulated user speech utterance. The Index Engine 101 IB is the entity that populates the Full-Text Index tables with indexes which correspond to the indexable units of text for the stored questions and corresponding answers. It scans through character strings, determines word boundaries, removes all noise words and then populates the full-text index with the remaining words. For each entry in the full text database that meets the selection criteria, a unique key column value and a ranking value are returned as well Catalog set 1013 is a file-system directory that is accessible only by an Administrator and Search Service 1010 Full-text indexes 1014 are organized into full- text catalogs, which are referenced by easy to handle names Typically, full-text index data for an entire database is placed into a single full-text catalog. The schema for the full-text database as described (FIG 7, Fig 7A, FIG 7B, FIG 7C, FIG 7D) is stored in the tables 1006 shown in FIG. 10. Take for example, the tables required to describe the structure the stored question/answer pairs required for a particular course. For each table — Course Table, Chapter Table, Section Table, there are fields - column information that define each parameters that make up the logical structure of the table. This information is stored in User and System tables 1006. The key values corresponding to those tables are stored as Full-Text catalogs 1013 So when processing a full-text search, the search engine returns to the SQL Server the key values of the rows that match the search criteria The relational engine then uses this information to respond to the query. As illustrated in Fig 10, a Full-Text Query Process is implemented as follows: 1 A query 1001 that uses a SQL fub-text construct generated by DB processor 186 is submitted to SQL Relational Engine 1002. 2 Queries containing either a CONTAINS or FREETEXT predicate are rewritten by routine 1003 so that a responsive rowset returned later from Full-Text Provider 1007 will be automatically joined to the table that the predicate is acting upon This rewrite is a mechanism used to ensure that these predicates are a seamless extension to a traditional SQL Server. 3. After this, Full-Text Provider 1007 is invoked, passing the following information for the query: a. Aft_search_conditιon parameter (this is a logical flag indicating a full text search condition) b. A name of a full-text catalog where a full-text index of a table resides c. A locale ID to be used for language (for example, word breaking) d. Identities of a database, table, and column to be used in the query e. If the query is comprised of more than one full-text construct; when this is the case Full-text provider 1007 is invoked separately for each construct. 4. SQL Relational Engine 1002 does not examine the contents of ft_search_condition. Instead, this information is passed along to Full-text provider 1007, which verifies the vabdity of the query and then creates an appropriate internal representation of the full-text search condition. 5. The query request/command is then passed to Querying Support 1011. 6. Querying Support 1012 returns a rowset 1009 from Full-Text Catalog 1013 that contains unique key column values for any rows that match the full-text search criteria. A rank value also is returned for each row. 7. The rowset of key column values 1009 is passed to SQL Relational Engine 1002. If processing of the query impbcates either a CONTAINSTABLEO or FREETEXTTABLE() function, RANK values are returned; otherwise, any rank value is filtered out. 8. The rowset values 1009 are plugged into the initial query with values obtained from relational database 1006, and a result set 1015 is then returned for further processing to yield a response to the user. At this stage of the query recognition process, the speech utterance by the user has already been rapidly converted into a carefully crafted text query, and this text query has been initially processed so that an initial matching set of results can be further evaluated for a final determination of the appropriate matching question/answer pair. The underlying principle that makes this possible is the presence of a full-text unique key column for each table that is registered for full-text searches. Thus when processing a full-text search, SQL Search Service 1010 returns to SQL server 1002 the key values of the rows that match the database. In maintaining these full-text databases 1013 and full text indexes 1014, the present invention has the unique characteristic that the full-text indices 1014 are not updated instantly when the full-text registered columns are updated. This operation is ebminated, again, to reduce recognition latency, increase response speed, etc. Thus, as compared to other database architectures, this updating of the full- text index tables, which would otherwise take a significant time, is instead done asynchronously at a more convenient time. Interface between NLE 190 and DB Processor 188 The result set 1015 of candidate questions corresponding to the user query utterance are presented to NLE 190 for further processing as shown in FIG. 4D to determine a "best" matching question/answer pair. An NLE/DBProcessor interface module coordinates the handbng of user queries, analysis of noun-phrases (NPs) of retrieved questions sets from the SQL query based on the user query, comparing the retrieved question NPs with the user query NP, etc. between NLE 190 and DB Processor 188. So, this part of the server side code contains functions, which interface processes resident in both NLE block 190 and DB Processor block 188. The functions are illustrated in Fig. 4D; As seen here, code routine 800 implements functions to extract the Noun Phrase (NP) bst from the user's question. This part of the code interacts with NLE 190 and gets the list of Noun Phrases in a sentence articulated by the user.. Similarly, Routine 813 retrieves an NP bst from the bst of corresponding candidate/paired questions 1015 and stores these questions into an (ranked by NP value?) array. Thus, at this point, NP data has been generated for the user query, as well as for the candidate questions 1015. As an example of determining the noun phrases of a sentence such as: "What issues have guided the President in considering the impact of foreign trade policy on American businesses?' NLE 190 would return the foUowing as noun phrases: President, issues, impact of foreign trade polity, American businesses, impact, impact of foreign trade, foreign trade, foreign trade poliy, trade, trade policy, policy, businesses. The methodology used by NLE 190 will thus be apparent to those skilled in the art from this set of noun phrases and noun sub-phrases generated in response to the example query. Next, a function identified as Get Best Answer ID 815 is implemented. This part of the code gets a best answer ID corresponding to the user's query. To do this, routines 813A, 813B first find out the number of Noun phrases for each entry in the retrieved set 1015 that match with the Noun phrases in the user's query. Then routine 815a selects a final result record from the candidate retrieved set 1015 that contains the maximum number of matching Noun phrases. Conventionally, nouns are commonly thought of as "naming" words, and specifically as the names of "people, places, or things". Nouns such as ohn, London, and computer certainly fit this description, but the types of words classified by the present invention as nouns is much broader than this. Nouns can also denote abstract and intangible concepts such as birth, happiness, evolution, technology, management, imagination, revenge, politics, hope, cookery, sport, and literacy. Because of the enormous diversity of nouns compared to other parts of speech, the Appbcant has found that it is much more relevant to consider the noun phrase as a key bnguistic metric. So, the great variety of items classified as nouns by the present invention helps to discriminate and identify individual speech utterances much easier and faster than prior techniques disclosed in the art. Following this same thought, the present invention also adopts and implements another linguistic entity — the word phrase - to facibtate speech query recognition. The basic structure of a word phrase — whether it be a noun phrase, verb phrase, adjective phrase - is three parts — [pre-Head string], [ Head] and [post-Head string]. For example, in the minimal noun phrase - "the children," "children" is classified as the Head of the noun phrase. In summary, because of the diversity and frequency of noun phrases, the choice of noun phrase as the metric by which stored answer is bnguistically chosen, has a sobd justification in applying this technique to the Engbsh natural language as well as other natural languages. So, in sum, the total noun phrases in a speech utterance taken together operate extremely well as unique type of speech query fingerprint. The ID corresponding to the best answer corresponding to the selected final result record question is then generated by routine 815 which then returns it to DB Process shown in FIG.4C. As seen there, a Best Answer ID I is received by routine 716A, and used by a routine 716B to retrieve an answer file path. Routine 716C then opens and reads the answer file, and communicates the substance of the same to routine 716D. The latter then compresses the answer file data, and sends it over data bnk 160 to cbent side system 150 for processing as noted earber (i.e., to be rendered into audible feedback, visual text/graphics, etc.). Again, in the context of a learning/instructional application, the answer file may consist solely of a single text phrase, but in other appbcations the substance and format wib be tailored to a specific question in an appropriate fashion. For instance, an "answer" may consist of a bst of multiple entries corresponding to a bst of responsive category items (i.e., a bst of books to a particular author) etc. Other variations will be apparent depending on the particular environment. Natural Language Engine 190 Again referring to Fig. 4D, the general structure of NL engine 190 is depicted. This engine implements the word analysis or morphological analysis of words that make up the user's query, as web as phrase analysis of phrases extracted from the query. As illustrated in Fig. 8A, the functions used in a morphological analysis include tokenizers 802A, stemmers 804A and morphological analyzers 806A. The functions that comprise the phrase analysis include tokenizers, taggers and groupers, and their relationship is shown in FIG 8 Tokenizer 802A is a software module that functions to break up text of an input sentence 801 A into a bst of tokens 803A In performing this function, tokenizer 802A goes through input text 801 A and treats it as a series of tokens or useful meaningful units that are typically larger than individual characters, but smaller than phrases and sentences These tokens 803A can include words, separable parts of word and punctuation Each token 803A is given an offset and a length. The first phase of tokenization is segmentation, which extracts the individual tokens from the input text and keeps track of the offset where each token originated from in the input text. Next, categories are associated with each token, based on its shape The process of tokenization is well-known in the art, so it can be performed by any convenient appbcation suitable for the present invention Following tokenization, a stemmer process 804A is executed, which can include two separate forms - inflectional and derivational, for analyzing the tokens to determine their respective stems 805A. An inflectional stemmer recognizes affixes and returns the word which is the stem A derivational stemmer on the other hand recognizes derivational affixes and returns the root word or words. While stemmer 804A associates an input word with its stem, it does not have parts of speech information Analyzer 806B takes a word independent of context, and returns a set of possible parts of speech 806A. As illustrated in Fig. 8, phrase analysis 800 is the next step that is performed after tokenization. Tokens 803 are assigned to parts of a speech tag by a tagger routine 804, and a grouper routine 806 recognizes groups of words as phrases of a certain syntactic type These syntactic types include for example the noun phrases mentioned earber, but could include other types if desired such as verb phrases and adjective phrases. SpecificaUy, tagger 804 is a parts-of-speech disambiguator, which analyzes words in context. It has a built-in morphological analyzer (not shown) that allows it to identify all possible parts of speech for each token The output of tagger 804 is a string with each token tagged with a parts-of-speech label 805 The final step in the bnguistic process 800 is the grouping of words to form phrases 807 This function is performed by the grouper 806, and is very dependent, of course, on the performance and output of tagger component 804 Accordingly, at the end of bnguistic processing 800, a bst of noun phrases (NP) 807 is generated in accordance with the user's query utterance This set of NPs generated by NLE 190 helps significantly to refine the search for the best answer, so that a single- best answer can be later provided for the user's question The particular components of NLE 190 are shown in FIG. 4D, and include several components Each of these components implement the several different functions required in NLE 190 as now explained Initialize Grouper Resources Object and the Library 900 — this routine lnitiahzes the structure variables required to create grouper resource object and bbrary. Specifically, it initializes a particular natural language used by NLE 190 to create a Noun Phrase, for example the Engbsh natural language is lnitiahzed for a system that serves the Engbsh language market. In turn, it also creates the objects (routines^) required for Tokenizer, Tagger and Grouper (discussed above) with routines 900A, 900B, 900C and 900D respectively, and lnitiahzes these objects with appropriate values. It also allocates memory to store all the recognized Noun Phrases for the retrieved question pairs. Tokenizing of the words from the given text (from the query or the paired questions) is performed with routine 909B — here all the words are tokenized with the help of a local dictionary used by NLE 190 resources. The resultant tokenized words are passed to a Tagger routine 909C. At routine 909C, tagging of ab the tokens is done and the output is passed to a Grouper routine 909D The Grouping of all tagged token to form NP bst is implemented by routine 909D so that the Grouper groups all the tagged token words and outputs the Noun Phrases Un-iniuabzing of the grouper resources object and freeing of the resources, is performed by routines 909EA, 909EB and 909EC These include Token Resources, Tagger Resources and Grouper Resources respectively. After lnitiabzation, the resources are freed The memory that was used to store all Noun Phrases are also de-allocated Additional Embodiments In a e-commerce embodiment of the present invention as illustrated in FIG. 13, a web page 130 contains typical visible bnks such as Books 131, Music 132 so that on cbcking the appropriate bnk the customer is taken to those pages. The web page may be implemented using HTML, a Java applet, or similar coding techniques which interact with the user's browser. For example, if customer wants to buy an album C by Artist Albert, he traverses several web pages as follows: he first cbcks on Music (FIG. 13, 136), then clicks on Records (FIG. 14, 145). As illustrated in FIG. 15, this brings up another web page 150 with bnks for Records 155, with sub-categories — Artist 156, Song 157, Title 158, Genre 159. The customer must then cbck on Artist 156 to select the artist of choice. This displays another web page 160 as illustrated in FIG. 16. On this page the various artists 165 are bsted as illustrated - Albert 165, Brooks 166, Charbe 167, Whyte 169 are bsted under the category Artists 165. The customer must now cbck on Albert 166 to view the albums available for Albert. When this is done, another web page is displayed as shown in FIG. 17. Again this web page 170 displays a similar look and feel, but with the albums available 176, 177, 178 bsted under the heading Titles 175. The customer can also read additional information 179 for each album. This album information is similar to the bner notes of a shrink-wrapped album purchased at a retail store. One Album A is identified, the customer must cbck on the Album A 176. This typically brings up another text box with the information about its availabibty, price, shipping and handling charges etc. When web page 130 is provided with functionabty of a NLQS of the type described above, the web page interacts with the cbent side and server side speech recognition modules described above. In this case, the user initiates an inquiry by simply clicking on a button designated Contact Me for Help 148 (this can be a bnk button on the screen, or a key on the keyboard for example) and is then told by character 144 about how to ebcit the information required. If the user wants Album A by artist Albert, the user could articulate "Is Album A by Brooks available?" in much the same way they would ask the question of a human clerk at a brick and mortar facibty. Because of the rapid recognition performance of the present invention, the user's query would be answered in real-time by character 144 speaking out the answer in the user's native language. If desired, a readable word balloon 149 could also be displayed to see the character's answer and so that save/print options can also be implemented. Similar appropriate question/answer pairs for each page of the website can be constructed in accordance with the present teachings, so that the customer is provided with an environment that emulates a normal conversational human-bke question and answer dialog for all aspects of the web site Character 144 can be adjusted and tailored according to the particular commercial appbcation, or by the user's own preferences, etc to have a particular voice style (man, woman, young, old, etc ) to enhance the customer's experience In a similar fashion, an articulated user query might be received as part of a conventional search engine query, to locate information of interest on the internet in a similar manner as done with conventional text queries. If a reasonably close question/answer pair is not available at the server side (for instance, if it does not reach a certain confidence level as an appropriate match to the user's question) the user could be presented with the option of increasing the scope so that the query would then be presented simultaneously to one or more different NLEs across a number of servers, to improve the bkebhood of finding an appropriate matching question/answer pair. Furthermore, if desired, more than one "match" could be found, in the same fashion that conventional search engines can return a number of potential "hits" corresponding to the user's query. For some such queries, of course, it is bkely that real-time performance wiU not be possible (because of the disseminated and distributed processing) but the advantage presented by extensive supplemental question/answer database systems may be desirable for some users It is apparent as web that the NLQS of the present invention is very natural and saves much time for the user and the e-commerce operator as well. In an e-support embodiment, the customer can retrieve information quickly and efficiendy, and without need for a live customer agent For example, at a consumer computer system vendor related support site, a simple diagnostic page might be presented for the user, along with a visible support character to assist him/her The user could then select items from a "symptoms" page (l e , a "monitor" problem, a "keyboard" problem, a "printer" problem, etc ) simply by articulating such symptoms in response to prompting from the support character. Thereafter, the system will direct the user on a real-time basis to more specific sub-menus, potential solutions, etc for the particular recognized complaint. The use of a programmable character thus abows the web site to be scaled to accommodate a large number of hits or customers without any corresponding need to increase the number of human resources and its attendant training issues. As an additional embodiment, the searching for information on a particular web site may be accelerated with the use of the NLQS of the present invention. Additionally, a significant benefit is that the information is provided in a user-friendly manner through the natural interface of speech. The majority of web sites presendy employ bsts of frequendy asked questions which the user typicaby wades item by item in order to obtain an answer to a question or issue. For example, as displayed in FIG. 13, the customer clicks on Help 133 to initiate the interface with a set of bsts. As illustrated in FIG. 18, a web site plan for typical web page is displayed. This illustrates the number of pages that have to be traversed in order to reach the bst of Frequendy-Asked Questions. Once at this page, the user has to scroll and manuaby identify the question that matches his/her query. This process is typically a laborious task and may or may not yield the information that answers the user's query. The present art for displaying this information is illustrated in FIG. 18. This figure identifies how the information on a typical web site is organized: the Help bnk (FIG. 13, 133) typically shown on the home page of the web page is illustrated shown on FIG. 18 as 180. Again referring to FIG. 18, each sub- category of information is bsted on a separate page. For example, 181 bsts sub-topics such as 'First Time Visitors', 'Search Tips', 'Ordering', 'Shipping', Υour Account' etc. Other pages deal with 'Account information' 186, 'Rates and Pobcies' 185 etc. Down another level, there are pages that deal exclusively with a sub-sub topics on a specific page such as 'First Time Visitors' 196, 'Frequently Asked Questions' 195, 'Safe Shopping Guarantee' 194, etc. So if a customer has a query that is best answered by going to the Frequendy Asked Questions bnk, he or she has to traverse three levels of busy and cluttered screen pages to get to the Frequendy Asked Questions page 195. Typically, there are many bsts of questions 198 that have to be manually scrolled through. While scrolbng visually, the customer then has to visually and mentally match his or her question with each bsted question. If a possible match is sighted, then that question is cbcked and the answer then appears in text form which then is read. In contrast, the process of obtaining an answer to a question using a web page enabled with the present NLQS can be achieved much less laboriously and efficiently. The user would articulate the word "Help" (FIG. 13, 133). This would immediately cause a character (FIG. 13, 134) to appear with the friendly response "May I be of assistance. Please state your question?" . Once the customer states the question, the character would then perform an animation or reply "Thank you, I will be back with the answer soon". After a short period time (preferably not exceeding 5-7 seconds) the character would then speak out the answer to the user's question. As illustrated in FIG. 18 the answer would be the answer 199 returned to the user in the form of speech is the answer that is paired with the question 195. For example, the answer 199: "We accept Visa, MasterCard and Discover credit cards", would be the response to the query 200 "What forms of payments do you accept?" Another embodiment of the invention is illustrated in FIG. 12. This web page iUustrates a typical website that employs NLQS in a web-based learning environment. As illustrated in FIG. 12, the web page in browser 120, is divided into two or more frames. A character 121 in the bkeness of an instructor is available on the screen and appears when the student initiates the query mode either by speaking the word "Help" into a microphone (FIG. 13,134) or by cbcking on the link 'Cbck to Speak' (FIG. 12, 128). Character 121 would then prompt the student to select a course 122 from the drop down bst 123. If the user selects the course 'CPlusPlus', the character would then confirm verbally that the course "CPlusPlus" was selected. The character would then direct the student to make the next selection from the drop-down bst 125 that contains the selections for the chapters 124 from which questions are available. Again, after the student makes the selection, the character 121 confirms the selection by speaking. Next character 121 prompts the student to select 'Section' 126 of the chapter from which questions are available from the drop down bst 127. Again, after the student makes the selection, character 121 confirms the selection by articulating the 'Section' 126 chosen. As a prompt to the student, a bst of possible questions appear in the bst box 130. In addition, tips 129 for using the system are displayed. Once the selections are all made, the student is prompted by the character to ask the question as follows: "Please ask your query now". The student then speaks his query and after a short period of time, the character responds with the answer preceded by the question as follows: "The answer to your question is as follows: ". This procedure allows the student to quickly retrieve answers to questions about any section of the course and replaces the tedium of consulting books, and references or indices. In short, it is can serve a number of uses from being a virtual teacher answering questions on-the-fly or a flash card substitute. From prebminary data available to the inventors, it is estimate that the system can easily accommodate 100 - 250 question/answer pairs while still achieving a real-time feel and appearance to the user (i.e., less than 10 seconds of latency, not counting transmission) using the above described structures and methods. It is expected, of course, that these figures wiU improve as additional processing speed becomes available, and routine optimizations are employed to the various components noted for each particular environment. Again, the above are merely illustrative of the many possible appbcations of the present invention, and it is expected that many more web-based enterprises, as well as other consumer appbcations (such as inteUigent, interactive toys) can utibze the present teachings. Although the present invention has been described in terms of a preferred embodiment, it will be apparent to those skilled in the art that many alterations and modifications may be made to such embodiments without departing from the teachings of the present invention. It wiU also be apparent to those skiUed in the art that many aspects of the present discussion have been simpbfied to give appropriate weight and focus to the more germane aspects of the present invention. The microcode and software routines executed to effectuate the inventive methods may be embodied in various forms, including in a permanent magnetic media, a non-volatile ROM, a CD- ROM, or any other suitable machine-readable format. Accordingly, it is intended that the all such alterations and modifications be included within the scope and spirit of the invention as defined by the foUowing claims. What is claimed is:

Claims: 1. A machine executable program for assisting a computing system to effectuate distributed voice query recognition comprising: a first audio signal receiving routine for receiving user speech utterance signals representing speech utterances to be recognized, said speech utterances including sentences comprised of one or more words; and a first signal processing routine adapted to generate representative speech data values from said speech utterance signals, said representative speech data values being characterized by a first data content that is substantiaUy inadequate by itself for permitting recognition of words articulated in said speech utterance; and a formatting routine for rendering said representative speech data values into a format suitable for transmission over a communications channel to a second processing routine executing on a separate computing system; and wherein said first data content in said representative speech data values is used by said second processing routine to compute additional data content that when combined with said first data content is sufficient for completing recognition of words articulated in said speech utterance at said separate computing system. 2. The program of claim 1, wherein said program works within a browser program executing on said computing system as part of a cbent-server based system. 3. The program of claim 1, wherein said first signal processing routine is further adapted to handle a stream of continuous speech utterances, such that a plurabty of representative speech data values are generated for each of said speech utterances in real-time. 4. The program of claim 1 , wherein said first data content is sufficiendy smaU that said formatting routine can handle in real-time a continuous stream of representative speech data values that may be generated for a corresponding stream of speech utterances. 5. The program of claim 1, wherein each of said representative speech data values corresponds to a separate cepstral coefficient value for a corresponding frequency component of said user speech utterance signals, and said first data content corresponds to a set of said frequency components spanning an audible speech frequency range. 6. The program of claim 5, wherein said additional data content corresponds to a set of delta and acceleration coefficients computed from a corresponding set of said ceptstral coefficient values. 7. The program of claim 6, wherein said additional data content is generated by said second processing routine with less latency than would that resulting if said additional data content were generated by said first signal processing routine. 8. The program of claim 1, wherein signal processing functions required to generate said first data content and said additional data content can be aUocated between said first signal processing routine and second signal processing routine as needed based on computing resources available to said first and second signal processing routines respectively. 9. The program of claim 1 , wherein said first signal processing routine is a set of instructions executed by any one of a host microprocessor, an embedded processor, and/or a digital signal processor (DSP). 10. The program of claim 1 , wherein said first signal processing routine and said first audio signal are implemented as part of a desktop computing system, a portable computing system, a personal digital assistant (PDA), a ceU-phone, and/or an electronic interactive toy. 11. A machine executable program for use in a voice query recognition system that is distributed across a cbent system and a separate server system, the program comprising: a first audio signal receiving routine for receiving user speech utterance signals representing speech utterances to be recognized during a sequence of speech utterance evaluation time frames, said speech utterances including sentences comprised of one or more words; and a first signal processing routine adapted to generate representative speech data values for each speech utterance evaluation time frame during which speech utterance signals are received; a formatting routine for rendering said representative speech data values into a format suitable for transmission from the cbent system over a communications channel to a second processing routine executing on the server computing system; and wherein said representative speech data values constitute a minimum amount of information that can be used by said second processing routine to complete accurate recognition of said one or more words and said sentences. 12. The program of claim 11 , wherein said program works within a browser program executing on said computing system as part of a cbent-server based system. 13. The program of claim 11 , wherein each of said representative speech data values corresponds to a separate cepstral coefficient value for a corresponding frequency component of said user speech utterance signals, and said first data content corresponds to a set of said frequency components spanning an audible speech frequency range. 14. The program of claim 13, wherein said additional data content corresponds to a set of delta and acceleration coefficients computed from a corresponding set of said cepstral coefficient values. 15. The program of claim 11, wherein said second processing routine performs accurate recognition of said one or more words with less latency than would that resulting if said one or more words were recognized by said first signal processing routine. 16. A distributed voice recognition system comprising: a sound processing circuit adapted to receive a speech utterance and to generate associated speech utterance signals therefrom; and a first signal processing circuit adapted to generate a first set of speech data values from said speech utterance signals, said first set of speech data values being insufficient by themselves for permitting recognition of words articulated in said speech utterance; and a transmission circuit for formatting and transmitting said first set of speech data values over a communications channel to a second signal processing circuit; and said second signal processing circuit being configured to generate a second set of speech data values based on said speech data values, such that second set of speech data values contain sufficient information to be usable by a word recognition engine for recognizing words in said speech utterance. 17. The system of claim 16, wherein said second set of speech data values include said first set of speech data values and a derived set of speech data values, which derived set of speech data values are computed based on said first speech data values. 18. The system of claim 17, wherein said first set of data values are MFCC vector coefficients, and said derived set of speech data values are MFCC delta coefficients and a MFCC acceleration coefficients derived from said MFCC vector coefficients. 19. The system of claim 16, wherein said second set of speech data values can be generated by said second signal processing circuit in a time that is less than the combination of a first time which would be required by said first signal processing circuit to generate said second set of speech data values from said first set of speech data values combined with a second time which would be required by said transmission circuit to format and transmit said second set of speech data values. The system of claim 16, wherein said second set of speech data values can be generated by said second signal processing circuit in less time than that which would be required by said first signal processing circuit to generate said second set of speech data values from said first set of speech data values The system of claim 16, wherein signal processing responsibibties of said first and second signal processing circuits are aUocated such that said first signal processing circuit performs less than approximately V2 the required signal processing operations needed to convert said speech utterance signals into a form usable by a word recognition engine The system of claim 16, wherein signal processing functions required to generate said first and second set of speech data values can be aUocated between said first signal processing circuit and second signal processing circuit as needed based on computing resources available to said first and second signal processing circuits respectively The system of claim 16, wherein signal processing functions required to generate said first and second set of speech data values can be aUocated between said first signal processing circuit and second signal processing circuit as needed based on computing resources available to said first and second signal processing circuits respectively The system of claim 16, wherein signal processing functions performed by said first signal processing circuit and second signal processing circuit are configured based on (1) computing resources available to said first and second signal processing circuits, (11) performance characteristics of said transmission circuit, and (111) transmission latencies of said communications channel The system of claim 16, wherein said first signal processing circuit is also configured to assist said second signal processing circuit with signal processing computations required to generate said second set of speech data values The system of claim 16, wherein said first set of speech data values represent the least amount of data that can used by said second signal processing circuit to generate said second set of data values usable for a word recognition process 27 A system for assisting a cbent computing device to perform speech recognition in cooperation with a server computing device, the system comprising a speech utterance capture circuit for receiving a speech utterance and generating associated speech utterance signals, where said speech utterance can include an articulated sentence of one or more articulated words, and a speech utterance signal processing circuit, said signal processing being configurable to perform data extracting operations on said speech utterance signals to generate a set of frequency related speech utterance signals for said articulated sentence, and a transmission circuit for coding said set of frequency related speech utterance signals into a format suitable for transmission over a communications channel to the server, a receiving circuit for receiving a response to said articulated sentence through said commumcations channel from the server, said response being generated by said server using said set of frequency related speech utterance signals to perform a word recognition operation on said one or more articulated words and a sentence recognition operation on said articulated sentence, and wherein a latency associated with performing said speech recognition is minimized by optimizing an aUocation of signal processing responsibibties for said speech utterance signals between the cbent computing device and the server computing device 28. A system for assisting a cbent computing device to perform real-time speech recognition in cooperation with a server computing device, the system comprising: a sound processing circuit integrated within the client computing device, said sound processing circuit being adapted to receive a continuous speech utterance and to generate associated speech utterance signals therefrom, wherein said speech utterance can include an articulated sentence of one or more articulated words; and a first signal processing routine adapted to be executed by the cbent computing device, and which first signal processing routine is further adapted to continuously generate a set of speech based vector coefficients as needed from said speech utterance signals; and a transmission circuit coupled to the cbent computing device for coding said set of speech based vector coefficients into a format suitable for transmission over a communications channel to the server, said set of speech-based vector coefficients being transmitted in real-time as said speech utterances occur; a receiving circuit coupled to the cbent computing device for receiving a real-time response to said articulated sentence through said communications channel from the server; wherein said response is generated by said server substantiaUy on a real-time basis using said set of speech based vector coefficients to perform a second signal processing routine which completes a word recognition operation on said one or more articulated words, as well as a sentence recognition operation on said articulated sentence. 29 A distributed speech recognition system for processing a speech utterance comprising a first signal processing circuit associated with a cbent computing system, said first signal processing circuit being adapted to generate a first set of speech data values from speech utterance signals, wherein said first set of speech data values have a bmited data content to reduce processing and transmission latencies in the distributed speech recognition system, and a second signal processing circuit associated with a separate server computing system, said second signal processing circuit being configured to generate a second set of speech data values derived from said first set of speech data values, and being further configured to generate a combined speech data value set consisting of said second set of speech data values and said first set of data values, a word recognition circuit adapted to use said combined speech data value set and for generating recognizing words in the speech utterance 30 The system of claim 29, further including a sentence recognition circuit which recognizes an articulated sentence containing said recognized words 31 The system of claim 30, wherein said articulated sentence can include one of a number of predefined sentences recognizable by said system, and said articulated sentence is recognized by identifying a candidate set of potential sentences from said number of predefined sentences corresponding to said articulated sentence, and then comparing each entry in the candidate set of potential sentences to said articulate sentence to determine a matching recognized sentence 32 The system of claim 31, wherein said articulated sentence is processed by a natural language engine operating on said recognized words 33 The system of claim 32, wherein said articulated sentence is compared against said candidate set of potential sentences by examining noun phrases 34 The system of claim 31, wherein said candidate set of potential sentences are determined in part by a context dictionary loaded by said sentence recognition circuit in response to an operating environment presented by said system to a user 35. A system for recognizing speech information, comprising: storage means for storing one or more words to be recognized by the system based on the speech information; and means for capturing speech signals corresponding to the speech information; and first processing means for generating partiaUy recognized speech data from said speech signals, said first processing means performing a first signal processing operation on said speech signals, said first signal processing operation being insufficient to permit said partially recognized speech data to be correlated with said one or more words; and second processing means for generating recognizable speech data from said partiaUy recognized speech data, using a second signal processing operation, such that said recognizable speech data can be correlated with said one or more words, said second processing means being distinct and physically separated from said first processing means; and a non-permanent data transmission connection coupbng said first and second processing means; transmitting means for transmitting said partiaUy processed speech data signals from said first processing means through said non-permanent data transmission connection to said second processing means; wherein said speech information is correlated to said one or more words based on said first and second signal processing operations. 36. The system of claim 35, wherein said first processing means is located at a cbent site, and said second processing means is located at a remote server site. 37. The system of claim 36, wherein said storage means is also located at said server site. 38. The system of claim 35, wherein said non-permanent connection is either a circuit switched or packet switched connection. 39. The system of claim 35, wherein said non-permanent connection includes an intranet network bnking said first and second signal processing means. 40. The system of claim 35, wherein said non-permanent connection is a wireless communications channel. 41. The system of claim 35, wherein said first signal processing operation includes an operation for extracting spectral parameter vectors from said speech signals. 42. The system of claim 41, wherein said first signal processing operation further includes an operation for decomposing the spectral parameter vectors with a Mel- frequency transfer process to obtain MFCC coefficients for said spectral parameter vectors. 43. The system of claim 41, wherein said partially recognized speech data comprises an observation vector O t which includes said MFCC coefficients and delta and acceleration coefficients obtained from said spectral parameter vectors. 44. The system of claim 35, wherein said partiaUy recognized speech data includes an observation vector O t , and second signal processing operation includes a Viterbi decoding operation for mapping a sequence of said observation vectors O t with a speech data symbol. 45. The system of claim 44, wherein said second signal processing operation further includes a text conversion operation for converting said speech data symbol into one or more text words. 46. The system of claim 45, further wherein said second processing means performs a query for determining which of said one or more words correspond to said one or more text words. 47. The system of claim 35, wherein said second processing means further utibzes environment variables for determining which of said one or more text words correspond to such speech information. 48 A method of performing voice recognition comprising the steps of (a) receiving user speech utterance signals representing speech utterances to be recognized, said speech utterances including sentences comprised of one or more words, and (b) performing a partial recogmtion of said one or more words contained in said speech utterance signals with a first computing device to generate representative speech data values, and (c) formatting said representative speech data values into a format suitable for transmission over a communications channel from said first computing device to a second computing device, and wherein said representative speech data values contain sufficient data content such that a complete recogmtion of said one or more words can be completed by said second computing device 49 The method of claim 48, wherein said content is sufficiently smaU that said partial recognition and formatting can be performed in real-time so as to generate a continuous stream of representative speech data values 50 The method of claim 48, wherein complete recogmtion of said one or more words is achieved with less latency than that resulting if said complete recogmtion were performed by said first computing device 51 The method of claim 48, wherein signal processing functions required to perform said partial recognition can be aUocated between said first computing device and said second computing device as needed based on computing resources available to said first and second computing devices respectively 52 The method of claim 48, wherein said first computing device is part of a cbent computing system, and said second computing device is part of a server computing system, and said communications channel is a network 53 A method of performing distributed voice recognition comprising the steps of: (a) receiving user speech utterance signals representing speech utterances to be recognized during a sequence of speech utterance evaluation time frames, said speech utterances including sentences comprised of one or more words; and (b) generating representative speech data values with a first processing circuit for each speech utterance evaluation time frame during which speech utterance signals are received, (c) encoding said representative speech data values into a format suitable for transmission over a communications channel to a second processing circuit; and wherein said representative speech data values constitute a minimum amount of information that can be used by said second processing circuit to complete accurate recognition of said one or more words and said sentences. 54 The method of claim 53, wherein said recogmtion of said one or more words occurs in real-time 55 The method of claim 53, wherein each of said representative speech data values corresponds to a separate cepstral coefficient value for a corresponding frequency component of said user speech utterance signals, and said minimum amount of information corresponds to a set of cepstral coefficients for frequency components spanning an audible speech frequency range 56 The method of claim 55, wherein a set of delta and acceleration coefficients are computed from said cepstral coefficient values to complete recogmtion of said one or more words and said sentences. 57 The method of claim 53, wherein said second processing circuit performs accurate recognition of said one or more words with less latency than would that resulting if said one or more words were recogmzed by said first processing circuit. 58. A method of performing distributed speech recognition using a first computing device and a second computing device, the method comprising the steps of: (a) receiving a speech utterance at the first computing device; and (b) generating associated speech utterance signals from said speech utterance with the first computing device; and (c) generate a first set of speech data values from said speech utterance signals at the first computing device, said first set of speech data values being insufficient by themselves for permitting recognition of words articulated in said speech utterance; and (d) formatting said first set of speech data values at the first computing device to be compatible with a communications protocol used by a communications channel coupled to the first computing device; (e) transmitting said first set of speech data values through said channel to the second computing device; and (f) generating a second set of speech data values based on said speech data values, such that second set of speech data values contain sufficient information to be usable by a word recognition engine for recognizing words in said speech utterance. 59. The method of claim 58, wherein said second set of speech data values include said first set of speech data values and a derived set of speech data values, which derived set of speech data values are computed based on said first speech data values. 60. The method of claim 58, wherein said second set of speech data values can be generated by said second computing device in a time that is less than the combination of a first time which would be required by said first computing device to generate said second set of speech data values from said first set of speech data values combined with a second time which would be required to format and transmit said second set of speech data values. 61. The method of claim 58, wherein signal processing responsibibties of said first and second computing devices are aUocated such that said first computing device performs less than approximately V_ the required signal processing operations needed to convert said speech utterance signals into a form usable by a word recognition engine. 62. The method of claim 58, wherein signal processing functions performed by said first and second computing devices are configured based on: (i) computing resources available to said first and second computing devices; and (ϋ) transmission latencies of said communications channel. 63. The method of claim 58, wherein said first processing device is also configured to assist said second processing device with signal processing computations required to generate said second set of speech data values. 64. The method of claim 58, wherein said first set of speech data values represent the least amount of data that can used by said second processing device to generate said second set of data values usable for a word recognition process. 65. A method of performing distributed recognition of a speech utterance comprising the steps of: (a) generating a first set of speech data values from speech utterance signals at a first computing system, wherein said first set of speech data values have a bmited data content to reduce processing and transmission latencies; and (b) generating a second set of speech data values derived from said first set of speech data values at a second computing system, said second computing system being independendy operable from said first computing system; and (c) generating a combined speech data value set at said second computing system consisting of said second set of speech data values and said first set of data values; (d) generating a bst of recognized words in said speech utterance. 66. The method of claim 65, further including a step: (e) recognizing an articulated sentence containing said list of recognized words. 67. The method of claim 66, wherein said articulated sentence can include one of a number of predefined recognizable sentences, and said articulated sentence is recognized by identifying a candidate set of potential sentences from said number of predefined sentences corresponding to said articulated sentence, and then comparing each entry in the candidate set of potential sentences to said articulate sentence to determine a matching recognized sentence. 68. The method of claim 67, wherein said articulated sentence is processed by a natural language engine operating on said recognized words. 69. The method of claim 68, wherein said articulated sentence is compared against said candidate set of potential sentences by examining noun phrases. 70. The method .of claim 67, wherein said candidate set of potential sentences are determined in part by a context dictionary loaded in response to an operating environment presented to a user articulating said sentence. 71 An interactive learmng system adapted for responding to speech-based queries concerning topics addressed by such interactive learmng system, the system comprising a query file for storing a plurabty of topic query entries, each topic query entry including a query relating to one or more of the topics covered by the speech-based interactive learmng system, and an answer file for storing a plurabty of topic answer entries, each topic answer entry including an answer to one or more of said plurabty of topic query entries, such that each topic query entry has at least one associated topic answer entry, and a speech recognition system for generating recogmzed speech data from partiaUy processed speech data associated with a speech-based query concermng one of said topics, said partiaUy processed speech data being received from a remote speech capturing system, and a query formulation system for converting said recogmzed speech data into a search query suitable for identifying a topic query entry corresponding to said speech-based query, and for locating at least one topic answer entry best matching said speech-based query 72 The system of claim 71, wherein said remote speech capturing system is located at a client site, and said speech recogmtion system is distributed across said cbent site and a separate server site 73 The system of claim 71, wherein said speech recogmtion system is comprised of a first portion at a cbent based computing system for performing first signal processing operations on a speech input signal to create said partiaUy processed speech data, and a second portion at a server based computing system for performing a second signal processing operation for completing processing of said partiaUy processed speech data 74 The system of claim 71, wherein said query formulation system includes a natural language engine for parsing said speech-based query 75 The system of claim 71, wherein said query formulation system uses context parameters for recognizing said speech-based query 76 The system of claim 75, wherein said context parameters include any one or more of the foUowing a course, chapter, and/or section of said lesson information selected by said user during the interactive session 77. The system of claim 75, wherein said context parameters include any one or more visible text words or objects presented to said user during the interactive session. 78. The system of claim 75, wherein said context parameters are used for dynamically determining and loading an appropriate grammar and dictionary file to be used for said speech-based query. 79. The system of claim 78, wherein said response system further includes a text to speech process so that said topic answer entry is expressed in audible form to said user. 80 A speech based interactive learmng system comprising an instructional file containing instructional materials arranged in a hierarchical structure that includes at least a first level of instruction data and a second level of instruction data, and wherein users of such system can navigate said hierarchical structure and formulate a user query concermng instructional materials located at least at said second level of instruction data, further wherein said user query can be correlated with a corresponding instructional material question taken from a bst of predefined questions, said set of predefined questions being paired with a corresponding set of responsive answers, a speech recogmtion engine for generating recogmzed speech data from a user query, a query formulation engine for converting said recogmzed speech data into a search query suitable for identifying a corresponding instructional material question for said user query, and for locating a corresponding responsive answer for said corresponding instructional material question, wherein said search query is formulated using a natural language engine parsing words contained in said user query 81 The system of claim 80, wherein said natural language engine compares said user query with one or more of said bst of predefined questions to determine said corresponding instructional material question 82 The system of claim 80, where a set of potential questions corresponding to said user query is derived from said bst of predefined questions by partiaUy recogmzing said user query, and said corresponding instructional material question is derived by fuUy recognizing said user query 83 The system of claim 80, wherein said first level of instruction data is associated with one or more lesson chapters for a particular course, and said second level of instruction data is associated with one or more sections, said one or more sections being bnked to said one or more chapters 84 The system of claim 80, wherein a first portion of said speech recogmtion engine is located at a cbent platform, and a second portion of said speech recogmtion engine as well as said query formulation system are located at a separate computing system 85. The system of claim 80, further including an animated visible agent for assisting said user to navigate said instructional materials, and for articulating said corresponding responsive answer to said user. 86. The system of claim 80, wherein said query formulation system uses context parameters for recognizing said speech-based query, and which context parameters dynamicaUy determine an appropriate grammar and dictionary file to be used for said speech-based query. 87 A speech based system for assisting a user in connection with an interactive lesson tutorial having question and answer capabibty, the system comprising. a lesson file containing instructional materials arranged to include a bst of predefined questions and a corresponding bst of predefined answers for said lesson, and a speech recognition engine for generating recogmzed speech data from a user query pertaining to said lesson, and a query recogmtion engine for locating a corresponding predefined question for said user query using said recogmzed speech data, and a conversion engine for converting a corresponding predefined answer for said corresponding predefined question into a form perceptible to the user; said query recogmtion system being adapted to emulate a human response time in response to a user query, so that the user perceives interaction with such system in essentially the same way that would be experienced from interacting with a real human. 88 The system of claim 87, wherein said system responds to a user query in less than approximately 10 seconds for a list of predefined questions having at least 100 entries, not accounting for data transmission latencies between the user and the system 89 The system of claim 87, wherein said system further includes a visible interactive agent that receives and responds to said user query. 90 The system of claim 87, wherein said interactive agent is configured with an appearance and mannerism that emulates a corresponding human appearance and mannerism appropriate for said lesson tutorial. 91 The system of claim 87, wherein said query recogmtion engine uses context parameters for recogmzing said speech-based query, and said context parameters control a dynamical loading of an appropriate grammar and dictionary file to be used for said speech-based query 92 The system of claim 87, wherein said query recogmtion engine is distributed across multiple computing system so that said more than one lesson file is consulted for said user query 93. A method of implementing a speech-based interactive query system, including the steps of: (a) storing a plurabty of topic query entries, each topic query entry including a query relating to one or more of topics covered by the speech-based interactive learning system; and (b) storing a plurabty of topic answer entries, each topic answer entry including an answer to one or more of said plurabty of topic query entries, such that each topic query entry has at least one associated topic answer entry; and (c) generating recognized speech data associated with a speech-based query concerning one of said topics, such that said recognized speech data is generated by partial recognition processing of said speech-based query by a first signal processing routine executing at a first computing device, and then completing recognition of said speech-based query through processing performed by a second signal processing routine executing at a second computing device; and (d) converting said recognized speech data into a search query suitable for identifying a topic query entry corresponding to said speech-based query; and (e) locating at least one topic answer entry best matching said speech-based query. 94. The method of claim 93, wherein said first computing device is located at a cbent site, and said second computing device is located at a separate server site. 95. The method of claim 93, wherein during step (d), a natural language engine processes said speech data to assist in formulating said search query. 96. The method of claim 93, wherein during step (d) context parameters are used for formulating said search query, and said context parameters are used for dynamicaUy determining and loading an appropriate grammar and dictionary file to be used for said speech-based query. 97. The method of claim 93, further including a step (f): communicating said topic answer entry to a user in response to said speech-based query. 98. The method of claim 97, wherein said response undergoes a text to speech process so that said topic answer entry is expressed in audible form to a user. 99 A method of presenting an interactive lesson to a user comprising the steps of (a) arranging instructional materials in a hierarchical structure that includes at least a first level of instruction data and a second level of instruction data, and (b) presenting the instructional materials to a user so that the user can navigate said hierarchical structure and formulate a user query concermng instructional materials located at least at said second level of instruction data, (c) defining a bst of predefined questions for at least said second level of instruction data, said set of predefined questions being paired with a corresponding set of responsive answers, (d) generating recognized speech data from a user query for such interactive lesson, (e) converting said recognized speech data into a search query suitable for identifying a corresponding instructional material question for said user query, said search query being formulated with assistance from a natural language engine parsing words in said recognized speech data, and (f) identifying a corresponding responsive answer for said corresponding instructional material question 100 The method of claim 99, wherein during step (f) said natural language engine operat s on said recognized speech data as well as on one or more of said bst of predefined questions to determine said corresponding instructional material question 101 The method of claim 99, further including a step (e)' deriving a set of potential questions corresponding to said user query from said bst of predefined questions by partiaUy recogmzing said user query, and then identifying said corresponding instructional material question by fuUy recogmzing said user query 102 The method of claim 99, wherein said first level of instruction data is associated with one or more lesson chapters for a particular course, and said second level of instruction data is associated with one or more sections, said one or more sections being bnked to said one or more chapters 103 The method of claim 99, wherein step (d) is distributed across a cbent platform and a separate computing system 104 The method of claim 99, further including a step (g) displaying an animated visible agent for assisting said user to navigate said instructional materials, and for articulating said corresponding responsive answer to said user 105 The method of claim 99, wherein during step (e) context parameters are used for recognizing said speech-based query, and said context parameters are used for dynamically determining and loading an appropriate grammar and dictionary file to be used for said speech-based query. 106. A method of operating a speech-based lesson tutorial system having question and answer capabibty, the method comprising the steps of: (a) configuring a bst of retrievable predefined questions and a corresponding bst of predefined answers for said lesson; and (b) generating recognized speech data from a user query pertaining to said lesson; and (c) locating a corresponding predefined question for said user query using said recognized speech data; and (d) converting a corresponding predefined answer for said corresponding predefined question into a form perceptible to the user; and wherein steps (b) to (d) are performed so as to emulate a human response time in response to a user query, so that the user perceives interaction with such system in essentiaUy the same way that would be experienced from interacting with a real human. 107. The method of claim 106, wherein step (d) occurs in response to a user query in less than approximately 10 seconds for a bst of predefined questions having at least 100 entries, not accounting for data transmission latencies between the user and the tutorial system. 108. The method of claim 106, further including a step (e): presenting a visible interactive agent to receive and respond to said user query. 109. The method of claim 108, wherein said interactive agent is configured with an appearance and mannerism that emulates a corresponding human appearance and mannerism appropriate for said lesson tutorial. 110. The method of claim 106, wherein during step (c) context parameters are used for recognizing said speech-based query, and said context parameters are used for dynamicaUy determining and loading an appropriate grammar and dictionary file to be used for said speech-based query. 111. The method of claim 106, wherein step (c) occurs across multiple computing system so that said more than one lesson file is consulted for said user query. 12. A speech-enabled internet website operating on a server computing system and comprising: a receiving routine executing on the server computing system for receiving speech data associated with a user speech-based query, said speech data being characterized by a data content that is substantiaUy inadequate by itself for permitting recognition of words articulated in said speech query; and a speech recognition routine executing on the server computing system for completing recognition of said speech query using said speech data and said data content to generate a recognized speech query; and a web page having a bst of items, at least some of said bst of items being selectable by a user based on said recognized speech query. 113. The website of claim 112, wherein said web page displays an additional bst of one or more items based on said recognized speech query. 114. The website of claim 112, wherein said website is adapted so that the user can navigate and locate information of interest using said speech query. 115. The website of claim 112, wherein said bst of items include products and/or services offered by said website. 116. The website of claim 112, wherein said web page is implemented in HTML or as a Java applet. 117. The website of claim 112, wherein said website is further adapted to respond to a speech query concerning said bst of items by returning a text or speech articulated response. 118. The website of claim 112, wherein said website is further adapted to interact on a real-time basis in response to one or more continuous speech queries. 119. The website of claim 112, wherein said speech recognition routine can complete recognition of said speech query with less latency than would that resulting if said additional data content were generated by a cbent platform used by the user. 120. The website of claim 112, wherein said data content constitutes a minimum amount of information that can be used by said speech recognition engine to complete accurate recognition of words and sentences in said speech query. 121. The website of claim 112, wherein signal processing functions required to generate said recognized speech query can be aUocated between a cbent platform and the server computing system as needed based on computing resources available to said cbent and server computing systems respectively. 122. The website of claim 112, wherein the website also controls an interactive character agent presented to the user for assisting in handbng said speech query. 123. The website of claim 112, wherein said bst of items correspond to topics associated with an interactive lesson tutorial. 124. A speech-enabled internet website operating on a server computing system and comprising: a receiving routine executing on the server computing system for receiving speech data associated with a user speech-based query, said speech data being characterized by a first data content that is substantiaUy inadequate by itself for permitting recognition of words articulated in said speech query; and a speech recognition routine executing on the server computing system for completing recognition of said speech query using said speech data and said first data content to generate a recognized speech query; and a web page having a search engine for locating user selected information of interest, said search engine using a text query that is derived from said recognized speech query. 125. The website of claim 124, wherein said speech query is processed by more than one server computing system, so that multiple search engines are used for locating said information of interest. 126. The website of claim 124, wherein said web page includes a bst of one or more items associated with assisting a user to diagnose a product or service problem, and which one or more items are also selectable by a user speech-based query. 127. The website of claim 124, wherein said website provides and controls an agent for assisting a user to interact with said website. 128. The website of claim 124, wherein said bst of items correspond to topics associated with an interactive lesson tutorial. 129. A system for enabbng a user web browser program to interact with a website using speech utterances, the system comprising: a receiving routine for receiving speech data associated with a speech utterance generated at a cbent platform, said speech data being characterized by a bmited speech data content to reduce processing and transmission latencies; and a speech recognition routine executing on a server computing system for completing recognition of said speech utterance using said bmited speech data content to generate a recognized speech query; and a web page routine for presenting one or more web pages to the user web browser program, wherein data content for said one or more web pages perceived by the user is controlled by said recognized speech query. 130. The system of claim 129, wherein said recognized speech query can include one of a number of predefined sentences recognizable by said system, and said speech query is recognized by identifying a candidate set of potential sentences from a number of predefined sentences, and then comparing each entry in the candidate set of potential sentences to said speech query to determine a matching recognized sentence. 131. The system of claim 129, wherein said speech utterance is processed by a natural language engine. 132. The system of claim 130, wherein said speech utterance is compared against said candidate set of potential sentences by examining noun phrases. 133. The system of claim 130, wherein said candidate set of potential sentences are determined in part by a context dictionary loaded by said sentence recognition circuit in response to an operating environment presented by said system to a user. 134. The system of claim 130, wherein environment variables experienced by the user within the web browser program are used for recognizing said speech query. 135. The website of claim 129, wherein said bst of items correspond to topics associated with an interactive lesson tutorial. . A method of interacting with a web-connected server using a cbent browser program, the method comprising the steps of: (a) receiving speech data associated with a speech utterance articulated by a user of the cbent platform, said speech data being characterized by a bmited speech data content to reduce processing and transmission latencies; and (b) completing recognition of said speech utterance using said bmited speech data content to generate a recognized speech query at the web-connected server; and (c) presenting one or more web pages to the user web browser program, such that data content for said one or more web pages transmitted to the cbent browser program is controUed by said recognized speech query. . A method of presenting information from a set of one or more web pages associated with a server interacting through a browser program with a cbent platform, the method comprising the steps of: (a) partiaUy processing a speech utterance at the cbent platform to generate bmited data content speech data, said bmited data content speech data being configured to reduce processing and transmission latencies; and (b) completing processing of said speech utterance using said bmited speech data content to generate a recognized speech query at the server; and (c) presenting content for the set of one or more web pages to the browser program, under control of said recognized speech query. 138. A speech query recognition system comprising: a speech recognition engine for generating recognized words taken from an articulated speech utterance; and a natural language engine configured for bnguisticaUy processing said recognized words to generate search predicates for said articulate speech utterance; a query formulation engine adapted to convert said recognized words and said search predicates into a structured query suitable for locating a set of one or more corresponding recognized matches for said articulated speech utterance; and said natural language engine further being configured for UnguisticaUy processing said set of one or more corresponding recognized matches to determine a final match for said articulated speech utterance. 139. The system of claim 138, wherein said query formulation engine generates a first level query using said recognized words alone, and further customizes said first level query using said search predicates to generate a second level query. 140. The system of claim 139, wherein said natural language operates to generate said seach predicates during a time when said query formulation engine generates said first level query. 141. The system of claim 138, wherein said speech recognition engine uses a set of context specific dictionaries for recognizing said words. 142. The system of claim 138, wherein said natural language engines uses a set of context parameters for generating said search predicates. 143. The system of claim 138, wherein said speech recognition engine, said natural language engine, and said query formulation engine are implemented as routines executing on a server computing system. 144. The system of claim 138, wherein said bnguistic processing includes an operation for determining noun-phrases in said speech utterance. 145. The system of claim 138, wherein said natural language engine compares noun- phrases of said set of potential matches with noun-phrases of said speech utterance to determine said final match. 146. The system of claim 138, wherein said final match is determined in real-time. 147. The system of claim 146, wherein said speech utterance can correspond to one of more than 100 potential corresponding potential matches, and said final match is used for determining an articulated answer to said speech utterance in less than 10 seconds. 148. The system of claim 138, wherein said speech recognition is distributed across a client-server architecture. 149. The system of claim 148, wherein said cbent generates an amount of speech data that is optimized to reduce recognition latencies. 150. The system of claim 138, wherein said recognized speech utterance is used for controlbng a web page. 151. The system of claim 138, wherein said structured query is a full text query containing SQL search predicates. 152. The system of claim 138, wherein said corresponding potential matches are retrieved from a relational database that is updated asynchronously to reduce retrieval latency. 153. A natural language processing system adapted for assisting recognition of a speech utterance, the system comprising: a text parsing routine for receiving text recognized by a speech recognition routine contained in the speech utterance, and for generating a first bnguistic analysis of said text; wherein said bnguistic analysis is used to identify a candidate set of potential matches for said speech utterance, and said text parsing routine being further configured for: (i) generating one or more second bnguistic analyses of text contained in said candidate set of potential matches; and (ii) comparing said first bnguistic analysis with each of said one or more second bnguistic analyses to identify a best match for said speech utterance. 154. The system of claim 153, wherein said text parsing routine is implemented by a software routine executing on a computing system. 155. The system of claim 153, wherein said text parsing routine is implemented by a DSP and associated executable code. 156. The system of claim 153, wherein said bnguistic processing includes an operation to determine noun-phrases. 157. The system of claim 156, wherein said best match corresponds to one of said candidate set of potential matches having the greatest number of noun-phrases in common with said speech utterance. 158. The system of claim 155, wherein said noun-phrases are determined and generated by breaking said text into tokens that are tagged and grouped according to 159. A method of recognizing a speech query comprising the steps of: (a) recognizing text in an articulated speech utterance; and (b) bnguisticaUy processing said recognized text to generate search predicates for said articulate speech utterance; (c) generating a query to identify a potential match for said speech utterance, said query being based on said recognized text and said search predicates; (d) determining a final match for said speech utterance by bnguisticaUy comparing any potential matches identified by said query with said articulated speech utterance. 160. The method of claim 159, further including a step: (e) retrieving a matching response for said final match, which matching response is provided in audible form. 161. The method of claim 159, wherein steps (b) and (c) overlap in time. 162. The method of claim 161, wherein step (c) includes two sub-steps, including a step (c)' wherein a prebminary query is generated based on said recognized text, and a step (c)" wherein a final query is generated based on said prebminary query and said search predicates. 163. The method of claim 159, wherein said final match is determined by comparing noun-phrases of said speech utterance and said potential matches. 164. The method of claim 159, wherein step (a) occurs across a distributed computing platform, including a cbent device and a server device. 165. The method of claim 159, wherein steps (a) to (d) occur simultaneously across multiple servers in response to a speech utterance from a single cbent device.

Inventors: BABU BANDI RAMESH, BENNETT IAN M, GURURAJ PALLAKI, MORKHANDIKAR KISHOR, BABU, BANDI, RAMESH, BENNETT, IAN, M., GURURAJ, PALLAKI, MORKHANDIKAR, KISHOR
Applicants: BABU BANDI RAMESH, BENNETT IAN M, GURURAJ PALLAKI, MORKHANDIKAR KISHOR, PHOENIX SOLUTIONS INC, BABU, BANDI, RAMESH, BENNETT, IAN, M., GURURAJ, PALLAKI, MORKHANDIKAR, KISHOR, PHOENIX SOLUTIONS, INC.
Application year: 2000
Main classification: G10L15
IPC codes: G06F3, G06F17, G09B7, G10L13, G10L15, G10L21