NATURAL LANGUAGE INTERFACE CONTROL SYSTEM

Abstract: A natural language interface control system (206) for operating a plurality of devices (114) consists of a first microphone array (108), a feature extraction module (202) coupled to the first microphone array, and a speech recognition module (204) coupled to the feature extraction module, wherein the speech recognition module utilizes hidden Markov models. The system also comprises a natural language interface module (222) coupled to the speech recognition module (204) and a device interface (210) coupled to the natural language interface module (222), wherein the natural language interface module is for operating a plurality of devices coupled to the device interface based upon non-prompted, open-ended natural language requests from a user.

Description: NATURAL LANGUAGE INTERFACE CONTROL SYSTEM This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Pate;:* Application Serial No. 60/160,281, filed October 19, 1999, of Konopka, for A NATURAL LANGUAGE INTERFACE FOR PERSONAL ELECTRONIC PRODUCTS, which U.S. Provisional Patent Application is incorporated herein by reference. BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to speech recognition, and more specifically to natural language speech recognition. Even more specifically, the present invention relates to a natural language speech recognition system used to control an application. 2. Discussion of the Related Art Many have dreamed of a device that could completely bridge the gap or seam between man-made machines and humans. Speech recognition techniques have been used to enable machines to recognize human speech. For example, speech recognition technology is used in many applications, such as word processing, control of devices, and menu driven data entry. Most users prefer to provide the input speech in the form of a natural language. Natural language is written or spoken input that is in natural form such as if the user is actually conversing with the machine. In contrast, non-natural language is limited in syntax and structure. To communicate with the machine in non-natural language, the user must know and speak commands or requests according to the syntactic and semantic structure of the speech recognition engine. Advantageously, a natural language interface system permits the user to easily interface with the machine or system, since the user can simply speak in a conversational manner without having to remember the proper format to speak a command or request. Disadvantageous^, natural language interface systems are difficult to implement due to the complex and shifting "rules" of human natural language. Furthermore, natural language processing of the prior art has been inefficient and rigid in its ability to recognize the meaning of natural language utterances. As such, in order to limit the context of the user's natural language input and ease the processing of the input speech, conventional natural language interface systems are dialog-based or prompt-driven. The natural language interface controls the context of the speech being input to the system. For example, natural language interfaces have been implemented as automated phone systems, such as an automated natural language airline reservation system. Such systems prompt the user to speak within a certain context. For example, the natural language system asks the user what city would the user like to fly. As such, the system dictates to the user the context of the speech it expects. Thus, the natural language interface system will look for natural language indicating names of cities. Next, the system will prompt the user to speak what date the user would like to fly Again, the context of the response is dictated by the natural language interface system Disadvantageously, the user is unable to provide open-ended information or an open ended request If the received speech data is not within the context as prompted by the system, the system will either ignore the request, inform the user that the response is not understood or potentially misinteφret the request as falling within the context of the prompt What is needed is an efficient natural language system in which the context is not limited by the natural language processing, but is limited by the user's speech The present mvenuon advantageously addresses the above and other needs SUMMARY OF THE INVENTION The present invention advantageously addresses the needs above as well as other needs by providing an open-ended natural language interface control system for controlling multiple devices whose context is not defined by the natural language interface, but by the direction of the user and the capabilities of the muluple devices In one embodiment, the invention can be characterized as a natural language interface control system for operating a plurality of devices compnsing a first microphone array, a feature extraction module coupled to the first microphone array, and a speech recognition module coupled to the feature extraction module, wherein the speech recognition module utilizes hidden Markov models The system also comprises a natural language interface module coupled to the speech recognition module and a device interface coupled to the natural language interface module, wherein the natural language interface module is for operaung a plurality of devices coupled to the device interface based upon non-prompted, open-ended natural language requests from a user In another embodiment, the invention can be characterized as a method of speech recogniuon compnsing the steps of searching for an attenUon word based on a first context including a first set of models, grammars, and lexicons, and switching, upon finding the attenUon word, to a second context to search for an open-ended user request, wherein second context includes a second set of models, grammars, and lexicons In a further embodiment, the mvenuon can be characterized as a method of natural language control of one or more devices, and a means for pracUcing the method, the method compnsing the steps of receiving an attenUon word, wherein the attenUon word indicates that an open-ended, natural language user request will be received, receiving the open-ended, natural language user request, matching the open-ended natural language request with the most likely command corresponding the open-ended natural language request, and transmitting the command to a respecUve one of the one or more devices BRIEF DESCRIPTION OF THE DRAWINGS The above and other aspects, features and advantages of the present mvenuon will be more apparent from the following more particular description thereof, presented in conjunction with the following drawings wherein FIG 1 is a system level block diagram of a natural language interface control system (NLICS) in accordance with one embodiment of the invenUon, FIG 2 is a functional block diagram of a remote unit of the natural language interface control system (NLICS) of FIG 1 in accordance with another embodiment of the mvenuon, FIG 3 is a functional block diagram of a base station umt of the natural language interface control system (NLICS) of FIG 1 in accordance with a further embodiment of the invenUon, and FIG 4 is a flowchart for the steps performed in the natural language interface algorithm of the natural language interface control system of FIGS 1 through 3 Corresponding reference characiers indicate corresponding components throughout the several views of the drawings DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following description of the presently contemplated best mode of practicing the invention is not to be taken in a limiting sense, but is made merely for the purpose of describing the general pπnciples of the invention The scope of the mvenuon should be determined with reference to the claims Referring first to FIG 1, a system level block diagram is shown of a natural language interface control system in accordance with one embodiment of the mvenuon Shown is the natural language interface control system 102 (also referred to as the NLICS 102) having a remote umt 104 and a base umt 106 (also referred to as a base staUon 106) The remote umt 104 has a linear microphone array 108 and a speaker 112 and the base unit 106 has a planar microphone array 110 The remote umt 104 is coupled to muluple devices 114 controllable via the natural language interface control system 102 Furthermore, the base umt 106 is coupled to an external network 116 In operation, the natural language interface control system 102 eliminates the seam between the muluple devices 114 and the user for control purposes The natural language interface control system 102 provides a natural language interface such that a user may control one or more of the muluple devices 114 by simply speaking in a natural, conversational manner to the natural language interface control system 102 The NLICS 102 is able to inteφret the natural language request of the user and issue the appropriate command to the respective devιce(s) to effect the user's request For example, in a home application, the devices 114 may include a television, a stereo, a video cassette recorder (VCR), a digital video disk (DVD) player, etc When the user wishes to operate one of the devices 114, the user simply speaks, "I wanna watch TV, or another natural language equivalent The NLICS 102 includes a speech recogniuon module utilizing hidden Markov models (HMMs), as known and understood in the art, to detect the speech and uses a natural language interface to inteφret the natural language and determine the probability of what the appropπate user request is The natural language interface utilizes probabihsUc context free grammar (also referred to as PCFG) rules and lexicons that are stored for each of the respecUve devices 114 As such, the natural language interface module includes a device abstraction module that contains an abstraction of each device 114 that the NLICS 102 is designed to interface Thus, each device 114 is abstracted into a set of commands that are understandable by the respecUve devices 114 Furthermore, each abstraction is associated with individual grammars and lexicons specific to the respecUve device Once the request is determined with the desired level of confidence, the natural language interface module issues a sequence of command(s) to the appropπate devιce(s) to effect the user's request For example, in response to a user's request of "I wanna watch TV, the natural language interface module will issue command(s) to the appropπate devιce(s) to turn on the television and amplifier, set the television and amplifier to the proper modes, and set the volume to an appropπate level It also updates the states and settings of these devices in its internally maintained abstracϋons The command may even turn the television to a preferred channel as learned by the NLICS 102 or as requested by Uie user in the open ended natural language request As a further example, the user may request specific information, such as "Do you have the album 'Genesis' " to which the system would respond "Yes" The user could then respond "Play that", or "Play the album Genesis" The system would respond by turning on the CD jukebox and the amplifier, setting the proper mode for the amplifier, setting the proper volume level, selecung the proper album and finally, playing the album It would also update the internally maintained states and settings of the device abstracuons as well as the user's profile Preferably, this command signal is transmitted via a radio frequency (RF) link or an Infrared (IR) link, as are known in the art Speech recogniuon techniques are well known in the art and the control of devices based upon spoken commands is known For example, applications exist where a user speaks a predetermined speech command to a speech recogniuon control system, for example, the user speaks, "Turn on" to a controlled television set In response, the TV is turned on However, such approaches do not take advantage of the use of natural language or conversauonal language, nor abstract the devices under control to deπve dialog context If the exact predetermined voice command is not issued, then the system will not issue the command In contrast, this embodiment implements a natural language interface module which is used to determine probabi sUcally the most likely meaning of the spoken utterance and issue the appropπate command(s) Thus, the lnstructtons from the user come in a very conversational manner without having to remember a specified command signal For example, if the user states "hey, lets watch TV, "I wanna watch TV, "turn on the TV, "whattya say we watch a little television", the system will use ixs natural language interface module to probabilisucally determine that the user is requesung to watch the television, and will issue an appropπate set of command(s) that the television and other appropπate devices will understand Thus, advantageously, the physical interface or seam between the device 114 and the user is eliminated For example, the user does not even need to know how to operate the device 114 in quesUon For example, the user may not know how to operate the DVD player, however, the user can simply say, "I want to watch a DVD" and a command signal may be sent to power on the DVD player and begin playing the DVD within the player Furthermore, the natural language interface module disambiguates the user's request if it is not sure what the request means For example, the request may be "I want to watch a movie" The natural language interface module does not know if the user would like to watch a movie on the DVD player, the VCR or a television movie In such cases, the natural language interface module includes a feedback module (e g a text-to-speech module) and a feedback mechanism such as a speaker to ask the user to clarify the request For example, the natural language interface module will ask in response to such a request, "Do you want to watch a movie on the DVD, VCR or television 9 " At which point the user may reply "DVD", for example As such, the system is a true "natural language interface" that can accept "open- ended" requests The natural language interface control system 102 is not a "closed-ended" system that is pπmaπly dialog dπven or prompt dπven For example, in known natural language systems, the conversaUon must be controlled by the system by prompung the user to provide certain informauon that the system will then try to identify For example, in a natural language based airline reservauon system, the system will guide the user through the dialog such that the context is constrained by the quesϋons asked by the system For example, the system will ask, "To what city would you like to fly 7 " Then the user would respond, in natural language, with the desunauon city and the system will essenually try to understand the response by trying to match the response with the names of ciues Then the system will prompt the user by asking "What date would you Id e to leave?" and the system will then constrain the context of the search and analysis of the incoming text stπngs based on what it is expecung to receive, I e , dates In contrast, with respect to the NLICS 102, the user, not the system, miuates the dialog The user simply states "I want to hear some music" with no prompung from the NLICS 102 The context of the search is not constrained by the prompung of the system, but is constrained by the abilities of the devices 114 controlled by the NLICS 102 Thus, the user may ask for the NLICS 102 to perform any of the tasks that each of the controlled devices is capable of performing If, for example, the user asks the NLICS 102 to perform a funcUon that is not available from the controlled devices, e g , if the user says "Make me some breakfast", the NLICS 102 is not able to effect such a request because it is not within the programmed funcuonality of the controlled devices For example, the NLICS 102 will properly inteφret phrases within the abiliUes of the devices 114 and simply ignore other requests Advantageously, the feedback portion of the natural language interface module will alert the user that the request is not available In this embodiment, the natural language interface control system 102 is "always on", such that the user may speak a request at any time and the system will respond However, to get the attenuon of the NLICS 102, the user speaks an "attenuon word" followed by the request This funcuons to identify the user, to avoid false detecUons of requests and to disϋnguish between regular conversauon and background noise not intended for the NLICS This attenuon word notifies the NLICS 102 that following the attention word, a request will arπve As such, the microphone arrays employed by the NLICS only have to search for the attenuon word or words within the physical spare defined by the microphone arrays For example, if the attenuon word is programmed as "Mona", then the user's request becomes "Mona, I wanna watch TV " This greatly reduces the processing and searching by the microphone arrays Furthermore, individual users may have separate attention words specific to that user For example, within a household, a first user's attenuon word is "Mona" while a second user's attenUon word is "Thor" When the NLICS 102 hears the attenuon word "Mona", the system assumes that the first user is issuing the command For example, if the first user says, "Mona, I wanna watch TV, then the system will not only turn on the television (and other relevant devices), but the system will turn on the television to the first user's selected favoπte channel Note that this does not provide a true identificaUon, however, since the first user could say the second user's attention word This mechanism simply provides a means to tailor the expeπence of the NLICS 102 specifically to the likes, pronunciations and habits of individual users One feature that enables the NLICS 102 to funcUon efficiently is that each of the devices 114 coupled to the NLICS 102 are abstracted into a separate device abstracuon such that separate grammars and lexicons are stored for each of the devices 114 For example, as the natural language interface module determines that the request is for the DVD player, a grammar and lexico" specific to that particular context (I e , the context of die DVD player) is used to aid in the processing of the arπving acousUc data within the speech recogmuon module This provides a context switching feature in the speech recogniuon module In some embodiments, the NLICS 102 is set up such that models used in the speech recogmuon module for the HMMs and grammars can be streamed into use from a secondary source, such as a hard disk, CD-ROM, or DVD at run Ume Once the data is read in, it can be immediately used without any preprocessing As such, memory usage for the speech recogmuon module is improved since many models and grammars can be stored remotely of the memory of the NLICS 102 In other embodiments, the NLICS 102 is designed to be implemented as two separate units, for example, the remote umt 104 and the base umt 106 The base umt 106 functions as a "docking station" for the remote umt 104, which may be coupled to the base umt 106 via a universal seπal bus (USB) connecuon, for example In some embodiments, the remote umt 104 funcuons as a universal remote control for a vaπety of devices as is tradiUonally done, by providing buttons for the user to press Furthermore, the base unit 106 provides an external network interface for the NLICS 102 For example, the external network interface couples the NLICS to an external network 116, such as a home local area network (LAN), an Intranet or the Internet As such, the NLICS 102 may download addiUonal grammars, HMM models, device abstracuons, CD, DVD, television or other programming information and/or lexicons that are maintained in central databases within the external network 116 Addi onally, the base unit 106 functions as a secondary cache for the remote unit 104 The remote umt 104 includes a feature extracuon module, a speech recogmuon module, and a natural language interface module, as well as the device interface to the vaπous devices As such, the base umt 106 includes a memory that funcuons to hold addiUonal models, grammars, and lexicons to be used in die remote umt 104 The remote unit 104 includes a traditional two element linear microphone array 108 that receives acousuc signaling Also, the base unit 106 contains a planar microphone array 110 which listens to acoustic energy from a two-dimensional space The NLICS 102 advantageously uses both microphone arrays 108 and 110 to implement a three-dimensional microphone array such that together the two sets of microphone arrays 108 and 110 listen to a predefined three-dimensional physical space As such, a three-dimensional volume can be defined within a space, for example, the NLICS 102 can be configured to listen to a volume including a living room couch where a user may be sitting when operating respective devices As such, acoustical data coming from sources outside of this defined space will attenuate while acoustical data coming from within the defined space will be summed in phase Although the system has generally been descπbed above, a more detailed descπption of the natural language interface control system follows Referπng next to FIG 2, a funcuonal block diagram is shown of the remote umt 104 of the natural language interface control system 102 of FIG 1 in accordance with another embodiment of the invenUon Shown is the remote unit 104 including the linear microphone array 108, a feature extracuon module 202, a speech recogmuon module 204, a natural language interface control module 206, a system processing controller 208, a device interface 210, a base umt interface 212 (also referred to as a universal seπal bus (USB) interface 212), and a speaker 214 Also illustrated are the devices 114 The speech recogmuon module 204 includes a speech decoder 216, an N-gram grammar module 218, and an acousuc models module 220 The natural language interface control module 206 includes a natural language interface module 222, a probabilistic context free grammar module 224 (also referred to as the PCFG module 224), a device abstracuon module 226 and a feedback module 228 Although the system has been descπbed as two separate components, 1 e , the remote umt 104 and the base unit 106, the core funcUonality of the NLICS 102 may be implemented solely within the remote umt 104, although preferred embodiments uUhze both the remote umt 104 and the base unit 106 as separate units. As such, the remote unit 104 will be described first below, followed by a description of the base unit 106. Acousuc data enters the remote unit 104 via the linear microphone array 108, which is a two element narrow-cardioid microphone that localizes a source, i.e., the user, and discriminates against interfering noise. Such linear microphone arrays are well known in the art. The linear microphone array 108 samples the input speech data from each of the microphone elements, and then time aligns and sums this data in order to produce a signal-to-noise ratio (SNR)-enhanced representation of the incoming acoustic signal. The acousuc data is then passed to the feature extraction module 202, which is used to extract parameters or feature vectors representing information related to the incoming acoustic data. The feature extraction module 202 performs edge-detection, signal conditioning and feature extraction. According to one embodiment, speech edge detection is accomplished using noise estimation and energy detection based on the 0 th Cepstral coefficient and zero-crossing statistics. Feature extraction and signal conditioning consist of extracting Mel-frequency cepstral coefficients (MFCC), delta information and acceleration information. It is a 38 dimensional feature vector based on 12.8 ms sample buffers overlapped by 50%. Such feature extraction modules 202 and functionality are well understood in the art, and that one skilled in the art may implement the feature extraction module in a variety of ways. Thus, the output of the feature extracuon module 202 is a sequence of feature vectors. Next, generally, the speech recognition module 204 functions as a Hidden-Markov Model (HMM)-based continuous speech recognizer that has the ability to reject "unmodeled events", e.g. out-of vocabulary events, disfluencies, environmental noise, etc. The speech recognition module 204 is under the control of the natural language interface module 222 and can switch between different acoustic models and different grammars based on the context of die speech, as determined by the natural language interface control module 206. The speech recognition module 204 may be entirely conventional, although die speech recogniuon module 204 has several features which are advantageous for use in the NLICS 102. Furthermore, memory usage in the speech recognition module 204 has been optimized so that the memory requirement is mainly a reflection of the amount of acoustic speech model data used. A more detailed description follows of the speech recognition module 204 and the natural language interface control module 206. The feature vectors from the feature extraction module 202 are input to the speech recogniuon module 204, i.e., input to the speech decoder 216 of the speech recognition module (SRM) 204. Thus, the speech recognition module (SRM) 204 is responsible for requesting speech feature vectors from the feature extracuon module (FEM) 202 and finding the most likely match of the corresponding utterance with a set of speech models, while rejecting non-speech events, using an approach based on Hidden Markov Models (HMMs). The models used by the speech decoder 216 are stored in the acoustic models module 220 These models mav compnse context-dependent or independent phonetic models, sub word models or whole word models, e g monophones, biphones and/or tπphones In one embodiment, the speech decoder 216 may dynamically switch between different models, e g , the speech decoder 216 may switch between models based on tπphones and monophones This is in contrast to known systems, where there are a fixed number of states and Gaussians per state, t e the architecture of the respective phonemes is fixed In contrast, a selection between models based on monophones, biphones, and tnphones, as well as varying the architecture of these phonemes, e g , the number of states and the number of Gaussians per state for each type of phoneme (monophone, biphone, and tπphone) may be vaned for optimization in space, speed, and accuracy As is well understood in the art, the received utterances are analyzed with the models, e g , using a Viterbi algorithm, and scores are assigned representing how well the utterance fits the given models Furthermore, the models used by the speech decoder 216 are under direct control by the natural language interface control module 206, which is descπbed further below Additionally, two garbage-modeling techniques are utilized Garbage filler models are stored with the acoustic models module 220 to model background noises as well as disfluencies and "silences" These models are utilized by the speech decoder 216 in the rejection of out-of- vocabulary (oov) events The speech decoder 216 also rejects out-of-vocabulary (oov) events using an online garbage calculation It then returns the N-best candidates if their scores are very close Such out-of-vocabulary rejection is also well understood in the art In some embodiments, the rejection techniques have been improved compared to those known in the art The basic pπnciple behind HMM-based speech recogmtion systems is that an utterance is compared with a number of speech models (from the acoustic models module 220) in order to find the model that best matches the utterance This implies that the output of the speech recogmtion module 204 will be a reference to the model (e g word) with the best match However, this causes problems in cases where no models exist that represent the words spoken In such cases, an HMM-based system will typically still attempt to find the closest match between utterances and models and report the results In many cases this is unwanted, as any sound that is picked up by an open microphone will cause a reference to a model to be emitted To avoid this effect, it is sometimes preferred to determine whether the utterance is contained within m-vocabulary words or not For example, if a Viterbi score passes a threshold, the utterance is determined to be an m-vocabulary word If the Viterbi score of the utterance does not exceed the threshold, then the utterance is deemed out-of-vocabulary Such a Viterbi score is generated using the Viterbi algoπthm This algoπthm calculates a single best state sequence through an HMM and its corresponding probability, given an observation sequence However, expeπments have shown that this is not a very accurate rejection scheme Instead, many systems rely on compaπng the Viterbi score with another Viterbi score that is obtained by reprocessing the utterance through an alternative HMM whose task is to represent all out- of-vocabulary events or filler sounds, 1 e , using garbage models A garbage score can then be defined as the difference between the logaπthms of each of the two Viterbi scores divided by the number of frames in the utterance according to equation 1 below The garbage score reveals whether the utterance had a closer match with the word models or the out-of-vocabulary models Many vaπants have been proposed as to how to reject out-of-vocabulaiy events One observation is that peπods of silence in an utterance typically produce high Viterbi scores even for models that are supposed to model high-energy parts-of-speech To some extent, this can be avoided by providing an additional feature representing the energy of the speech signal in the feature extraction module 202 However, this still leads to lncoπect garbage score measurements If there is silence in the beginning or ending of an utterance and tins beginning or ending silence is not being modeled, it has been observed that the garbage scores are indeed affected The feature extraction module 202 performs speech detection such that the beginning and ending silences should not be included in the sample forwarded to die speech decoder 216 of the speech recogmtion module 204 However, finding the beginning and ending of an utterance becomes a complex task for utterances that begin or end with low-energy sounds An example of a group of sounds where this is a problem is the fricative Fπcaϋves are characteπzed as broadband, low energy noise, e g "white noise" A fricative, as known in the art, is a sound, as exemplified by such phonemes as "th", "sh", etc The feature extraction module 202 attempts to solve this problem by making its best efforts to find the beginning and ending samples To guarantee that low-energy sounds are included in the speech sample, the feature extraction module 202 includes a number of extra samples in the beginning and ending of the utterance In cases where there is no low-energy sound in the beginning or ending of an utterance, this implies that silence will be prepended and appended to the speech sample, assuming that the utterance was spoken in isolation, and hence the garbage scores in the speech decoder 216 become skewed To solve this problem, in one embodiment, each model is preceded and followed by a single- state silence model that "consumes" die frames of silence passed along from the feature extraction module 202 The speech decoder 216 then finds the sequence of models with the closest match and optimally aligns the silence models as well as the word-models with the utterance Now the start and end indices for the beginning and ending silence portions of the utterance can be obtained and removed Furthermore, the best matching word models are now kept and reprocessed without the preceding and following silence models, using only the pure-speech portion of the utterance Next, the out-of-vocabulary HMMs process the same portion of the utterance and the garbage scores can be calculated as, _ w - g ( i ) n - m ' where w is the logaπthm of the Viterbi score for the acoustic models of in-vocabulary words without preceding or following silence models and where no silence is included in the utterance Similarly, g is the loganthm of the corresponding score for the out-of-vocabulary HMM models Also, n is the total number of frames in the utterance and m is the number of frames that were consumed by the preceding and following silence models In summary, using this rejection technique, the system is better able to accurately isolate the speech portion of the utterance This has the effect of better isolating in-vocabulary words and rejecting out-of vocabulary events that begin or end with low energy sounds, such as fricatives, in companson to conventional rejection schemes The N-gram grammar module 218 includes the grammars used by the speech decoder 216 These grammars are the rules by which lexicons are bmlt and a lexicon is a dictionary consisting of words and dieir pronunciation entπes The specific grammars used by the speech decoder 216 are also controlled by the natural language interface module 222 In this embodiment, die N-gram grammar is configured to use multiple grammar types or a combinauon of grammar types For applications (e g , controlled devices with many controls and functions) that use a complex language it might be advantageous to use the tngram grammar option For smaller systems (e g , a device wiϋi very simple controls and functions), the bigram grammar option might constitute a better memory and accuracy tradeoff To provide a memory efficient representation of the bigram and tngram grammars, the allowed combinations of lexicon entπes can be expressed in terms of specific lexicon entry labels or word groups If any lexicon entry should be able to follow upon any lexicon entry, the ergodic grammar option can be used It is noted that the use of an N-gram grammar within a device that generally has a small footprint is not intuitive By a small footpπnt, it is meant that the system only has to recognize speech relaung to the controlled devices 114 coupled to the remote umt 104, such that it can classify the remaining speech as out-of-vocabulary However, the N-gram grammar module 218 allows for the use of multiple grammars and types even in the case of a speech recogmtion module 204 having a small footpπnt Another grammar that is mainly used for the rejection scheme of the speech decoder 216 is the word list grammar The word list grammar is used to recalculate the Viterbi score for a fixed sequence of words and a subset of an utterance The system incorporates the vaπous grammars in such a way that allows for "context switching" or the immediate switching between grammar types and sets of grammar rules under the control of the natural language interface module Being able to do so is important as the content of a person's speech is highly affected by context For example, only certain phrases (e g , the attention words descπbed above) are expected to begin a dialog while others could only follow upon a quesuon (e g , the natural language interface disambiguating an unclear request) In particular, this becomes evident when a speaker is targeting different audiences, and in the case of consumer electronics - different products, such as a television, a DVD player, a stereo, and a VCR As an attempt to keep the processing requirements low while increasing the speech recogmtion accuracy, the system provides a way to define contexts for which only certain grammar rules should apply If the context is known, die natural language interface module 222 can instruct the speech recogmuon module 204 to listen only to phrases that are expected For example, when the natural language interface module 222 has determined that the user is attempting to operate the DVD player, the speech recogmtion module 204 may be instructed to use the grammar type and grammar corresponding to the DVD player Thus, the speech decoder 216 will retπeve the proper grammar from the N-gram grammar module 218 Context switching can also be performed on a finer level where a flag for each grammar rule or lexicon entry is used to indicate which individual rules or words are to be enabled and disabled Further, for some system setungs and some grammar modes it might be preferred to limit the search for the best hypothesis to a set of lexicon entπes Defining several lexicons and referencing only the lexicon of interest can do this It is noted that since the speech recogmuon module 204 can dynamically change the grammar used given the context of die received speech, the lexicons are dynamically changed, since the lexicons depend on the selected grammar/grammars Depending on the size of the system, l e , how great the search needs to be in the speech decoder 216, the processing time can be reduced For medium to large size natural language interface control systems 102 (perhaps having many controlled devices 114), the processing time is greaϋy reduced using an efficient implementation of the Beam Search algoπthm This beam search algoπthm aims to keep the number of hypotheses at a mimmum duπng the Viterbi search algoπthm As such, all acuve hypotheses are compared at each discrete time step and the Viterbi score for the best hypothesis is calculated Pruning can then be accomplished by discarding any hypotheses whose scores fall below the maximum hypothesis score minus some pre-defined rejection threshold function This constrains the search based on hypotheses that are pruned and so will not be considered again in the following time steps until the score for the corresponding model states become high enough to pass the threshold Another problem associated with large speech recogmuon systems is the amount of memory required to store the speech models Fortunately, the number of sub word units (e g phonemes), used by the NLICS 102 is typically fixed and hence, more and more speech models will reference the same sub word models as the number of lexicon entπes grows By allowing lexicon entπes to reference the same model elements, e g sub word models, model states and/or Gaussians, the memory requirements can be kept to a mimmum The tradeoff is a slight increase in die computational resource required When this indirect model referencing is used, speech can be represented on any level of abstraction (e g phrases, words, sub words) Such abstractions can be combined to form more abstract units according to a lexicon, which in turn can be referenced in grammar definitions Token Passing is a well-known approach to tracking the best word hypotheses through an HMM As is known m the art, in connected word recogmuon systems, the last model state for the state sequence with the highest Viterbi score can be easily found once the processing of all frames of an utterance is completed However, this does not necessaπly provide the best state (or word) sequence To find the best state sequence, it is required to perform "back tracing" The traditional way of doing this is to let each state contain a pointer back to the previously best state for each frame Back tracing can then be performed by following the pointers back, starting with the last model state for the state sequence with the highest Viterbi score This means that if a system uses N states over T discrete time steps, the number of back pointers required is typically Λ This quickly becomes a high number and therefore leads to high memory requirements Vaπous methods have been proposed to minimize the memory requirements associated with stoπng such back-pointers, whereof some are based on the idea of passing "tokens" around to the vanous states instead of allocating memory on a per-state basis In accordance with one embodiment of the invention, instead of stoπng one token pointer in each state, the speech decoder 216 uses two arrays S, and S 2 to hold the token pointers for each state Array S, keeps the token pointers for each state and the previous frame, and S 2 keeps the token pointers for each state and the current frame When each state i "looks back" to find the previously best state/, two things can happen If the previous best state/ is a member of die same acoustic model as /, the token pointer for state/ in Sj is copied into position t in S 2 If this is not the case, a new token is created and stored in position ; in S 2 The new token gets the same contents as token / in S,, and in the token history, a reference to model m, i cm, is added Once all states have been processed for the current frame, the pointers to structures S, and S 2 , are swapped, and the process is repeated for the following frame Thus, tius token passing technique provides a highly memory efficient solution to an otherwise well-known problem in HMM-based speech recogmtion systems, the storage of back-pointers that allows for finding the best word sequence hypothesis once all speech data has been processed In some embodiments, a caching scheme is used for the lexicons stored in memory on the remote umt, e g , by the N-gram grammar module 218 A stated above, a lexicon is a dictionary consisting of words and their pronunciation entπes These pronunciations may be implemented as either phonetic spellings that refer to phonetic models, or to whole-word models A given word entry may contain alternate pronunciation entπes, most of which are seldom used by any single speaker This redundancy is echoed at each part-of-speech abstraction, creating even more entπes that are never uulized by a given speaker This implies that if lexicon entπes are sorted by their frequency of usage, there is a great chance that the words in an utterance can be found among the top n lexicon entnes As such, the cache is divided into different levels divided by frequency of use For example, frequently used lexicon entnes will be stored within the top level of the cache A caching scheme may devised in which the top 10% of the cache is used 90% of the time, for example Thus, according to an embodiment, a multi-pass search is performed where the most likely entπes are considered in the first pass If the garbage score from this pass is high enough to believe that the words actually spoken were contained in the set of most likely spellings, the speech decoder 216 reports the results to the calling function If this score is low, the system falls back to consideπng a wider range of spellings If the score from the first pass is high, but not high enough in order to be able to make a decision whether the coπect spellings, for the elements of the utterance, were contained in the set of most likely spellings, this is also reported back to the calling function, which might prompt the user for clarification If a lexicon spelling for a given part-of-speech is never used while some of its alternative spellings are frequentiy used, that spelling is put in a "trash can" and will never be considered for that user As such, rarely used spellings are not considered and the chance of confusing similar-sounding utterances with one of those spellings is reduced and the recogmtion accuracy is therefore increased Further, the caching scheme allows the system to consider less data and hence provides a great speed improvement Next, the natural language interface control module 206 will be descπbed in detail The natural language interface control module 206 includes the natural language interface module 222, the probabilisuc context free grammar (PCFG) module 224, the device abstracuon module 226, and the feedback module 228 Generally, die natural interface module (NLIM) 222 is responsible for inteφreting the user's requests within the context of the devices 114 under control and the user's usage history as defined by a set of probabilistic context-free grammar (PCFG) rules and device abstractions As such, the natural language interface module 222 asserts control over the speech recogmuon module 204 and the microphone array 108 search It does this by controlling the speech recogmuon module's 204 grammar, and therefore the lexicon under consideration It also controls system parameters as well as the cuπent state of its device abstractions, and current language references As descπbed above, die user initiates a dialog with the NLICS by speaking an attenuon word The prefeπed method of locating die attention word is descπbed with reference to FIG 3 The user then follows the attention word with an open-ended request constrained only by the capabilities of the devices coupled to the remote umt 104 The attenuon word alerts to the natural language interface module 222 the identity of the user so that the speech decoder can be instructed to use the proper grammar and models based upon the attenuon word, thus, the system can preconfigure itself to the speech pattern's (e g , the pronunciation, structure, habits etc ) of the user The speech recogmtion module 204 transcnbes the user's request, which is in namral, conversational language The utterance is transcπbed into a set of alternative hypothesis stπngs ordered by probability For example, the speech decoder 216 forwards the N best text stnnps to the namral language interface module 222 to be analyzed to determine the probable meamng of the utterance The namral language interface module 222 then parses the incoming strings by applying a set of probabilistic context free grammar (PCFGs) rules from the PCFG module 224 to find the most likely stπng, given the stnng's probability, the user's history, and the cunent system context These PCFG rules reflect the context of the user (based on the attenuon word) and also the context of the device to be operated (if already determined) The PCFGs are initially ordered in terms of frequency of usage as well as likelihood of use Over time, it tracks habits of individual users and improves rule probability esumations to reflect this data This data can be shared and combined with data from other systems and then redistπbuted via die collaboraUve coφus Furthermore, note that the NLICS includes two sets of grammars, one is the N-gram grammar of the speech recogmtion module 204 and the other is the probabilistic context free grammar module 224 of the natural language interface control module 206 Conventional systems only use one set of grammars, not a combination of N-gram grammar and PCFG rules which are mfeπed from data collected from man-machine dialog in the domain of personal electronic products Using the PCFG rules on the incoming text stπngs, the natural language interfaα module 222 reaches one of three conclusions (1) that it unambiguously understands and can comply with the user request, in which case it carπes out the command, (2) that is unambiguously understands and cannot comply with a user request, in which case it informs the user of this conclusion, and (3) that it cannot resolve an ambiguity in the request, in which case, it requests clarification from the user For example, in case 1, the namral language interface module 222 inteφrets an incoming stnng with a sufficiently high confidence level as a request to "Turn on the television" As such, the appropπate command within the device abstraction module 226 is retneved and transmitted to the controlled device 114 (I e , the television) The device abstraction module 226 includes all of the commands to effect the proper requests of the user in the format understandable by the television itself Typically, the command is transmitted via the device interface 210, e g , an IR transmitter, to the television In response, the television is powered on The second case is the case in which the user asks the NLICS to perform a task it can not perform For example, the user requests for the television to explode The feedback module (e g text-to-speech) 228 is instructed to play an audible message over the speaker alerting the user that the request can not be performed It is noted that ti.c feedback module 228 may simply display notices on a screen display instead of playing an audio signal over the speaker 214 In the third case, the ambiguity is resolved according to the kind of ambigmty encountered Thus, the namral language interface module 222 dtsambiguates the ambiguous request If the ambigmty anses due to a low confidence, it asks the user to affirm its conclusion For example, the speaker 214 plays, "Did you mean play the CD?" Alternatively, the natural language interface module 222 asks the user to repeat the request If the ambigmty anses due to a set of choices, it presents these alternatives to the user, e.g., "Did you want to watch a movie on the VCR or the DVD?" If the ambiguity arises because of the current context, the user is made aware of this, e.g., the user requests to play the DVD player when it is already playing. In the first two ambiguous situations, the system adjusts the user's profile to reflect the confidence with which a decision was made, as well as preference given a set of alternatives. In some embodiments, over time, these statistics are used to reorder the PCFG rules and entries in the relevant lexicon(s). This results in a faster, more accurate system, since the most likely entries will always be checked earlier and these more likely entries will produce a higher confidence. It is noted that when the natural language interface module 222 instructs the feedback module 228 to clarify the request, e.g., the speaker 214 plays "Did you mean to play a CD?", the natural language interface module 222 switches the context and grammar rules based on what it is expecting to receive at the microphone array 108. For example, the system will switch to a context of expecting to receive a "yes" or a "no" or any known variants thereof. When the user replies "yes", the natural language interface module 222 switches context back to the original state. As such, again, when the context changes, the natural language interface module 222 instructs the speech recognition module 204 to switch grammars, which will indirectiy cause the lexicons to change, since the grammar controls which lexicons are used. The namral language interface control module 206 also contains the device abstraction module 226. The device abstraction module 226 stores the abstractions for each device 114. As such, the commands for each device 114 and the objects that each device 114 can mampulate are stored here. It also relates these controls to the states that the devices can be in and the actions they can perform. The content of the device abstraction module 226 depends on the different devices that are coupled to the remote unit 104. The device abstraction module 226 also includes commands for other devices in order to operate another device. For example, if the user requests to play a DVD. then the instructions to power on die DVD player, cause the DVD to play are issued. Additionally, a command signal is sent to turn on the television, if it is not already on. The commands stored in the device abstraction module 226 are transmitted to the respective controlled device 214 via the device interface 210. In some embodiments, the device interface 210 is an IR or an RF interface. The NLICS can be implemented to control any device which is controllable via such an IR link. As long as the device abstraction has stored the commands to operate the specific device, the device does not realize that it is being controlled by a natural language interface. It simply dunks its remote control or a universal remote control has sent the signal. The system processing controller 208 operates as the controller and processor for the various modules in the NLICS. Its function is well understood in the art. Furthermore, the interface 212 is coupled to the system processing controller 208. This allows for connection to the base unit 106, or alternatively, to a computer The interface 212 may be any other type of link, either wireline or wireless, as known in the art It is noted that vaπous components of system, such as the feature extraction module 202, the speech recogmuon module 204 and the natural language interface control module 206 may be implemented in software or firmware, for example using an application specific integrated circuit (ASIC) or a digital signal processor (DSP) Refernng next to FIG 3, a functional block diagram is shown of a base umt or base stauon of the namral language interface control system of FIG 1 in accordance with a further embodiment of the invention Shown is the base unit 106 (also referred to as the base station 106) and die remote umt 104 including the linear microphone array 108 The base umt 106 includes the planar microphone array 110, a frequency localization module 302, a time search module 304, a remote interface 306 (also refened to as interface 306), die external network interface 308, and a secondary cache 310 The linear microphone amy 108 and the planar microphone array 110 combine to form a three-dimensional microphone array 312 (also refened to as a 3D microphone array 312) Also shown is the external network 116 coupled to the external network interface 308 In operation, the base unit 106 is intended as a docking stauon for the remote unit 104 (which is similar to a umversal remote control) The base unit 106 includes the external network interface 308 such that the NLICS can interface with an external network 116, such as a home LAN or the Internet either directly or through a hosted Internet portal As such, addiUonal grammars, speech models, programming information, IR codes, device abstractions, etc can be downloaded into die base unit 106, for storage in the secondary cache 310, for example Furthermore, the NLICS 102 may transmit its grammars, models, and lexicons to a remote server on the external network for storage This remote storage may become a repository of knowledge that mav be retneved by other such devices As such, the system will never get old, since lexicons will constandy be updated with the most cunent pronunciations and usages This enables a collaborative lexicon and or a collaborative coφus to be built since multiple namral language interface control systems will individually contπbute the external database in a remote server Furthennore, the NLICS 102 may download command signals for the device abstraction module of the remote unit 104 For example, a user would like to operate an older VCR that has an IR remote control manufactured by a different maker that the NLICS The base umt 106 simply downloads the commands that are stored for any number of devices These commands are then stored in the device abstraction module Also, the NLICS can submit feature vector data and labels associated with high-confidence utterances to die collaborative coφus This data can then be incorporated with other data and used to tram improved models that are subsequently redistnbuted This approach can also be used to incorporate new words into the collaborative coφus by submitting the feature vector data and its label, which may subsequendy be combined with other data and phonetically transcπbed using the forward-backward algoπthm This entry may then be added to the lexicon and redistnbuted The base unit 106 includes the planar microphone array 110 The planar microphone array 110 and the linear microphone array 108 of the remote umt 104 combine to form a three-dimensional array 312 Both arrays compnsε conventional point source locating microphone As is known in the art, a three-dimensional array is constructed by first constructing a planar array (e g , planar microphone array 110), then adding one or two microphone elements off of the plane of the planar array As such, the linear microphone array 108 becomes the additional one or two elements This enables the NLICS 102 to define a three dimensional search volume As such, the device will only search for speech energy within the volume Thus, the microphone arrays 108 and 110 will localize on a point within the search volume Speech energy, background noise, etc., outside of the search volume will be attenuated, while speech energy within the search volume will be summed Practically, the user will need to be positioned within a particular volume to control the vaπous devices For example, the search volume is configured to be the volume about a user's living room couch Both the linear microphone array 108 and the planar microphone anay 110 are controlled by the natural language interface module 222 A frequency localization module 302 and a time search module 304 are coupled to the 3D microphone array 110 The time search module 304 receives control signaling from the natural language interface module 222 within the remote umt 104 via the remote interface 306 The time search module 304 adds up time aligned buffers which are provided by the microphones Thus, the time search module 304 locates putative hits and helps to steer die 3D microphone anay 110 in the direcuon of the hit The funcUonality of the time search module 304 is well known in the art. The frequency localization module 302 is also under the control of the natural language interface module 222 The frequency localization module 302 implements a localization algoπthm as is known in the art The localizauon algoπthm is used to localize speech energy within the defined volume As such, speech energy oπgmaUng from outside of the localized point within the volume will attenuate (is out of phase), while speech energy from within the localized point will sum (is in phase) Thus, the localization takes advantage of construcuve interference and destructive interference in the frequency domain. In operation, the search module is used to do a coarse search for attention words. If the speech energy passes a threshold, then a fine search is done by the localization module If it passes the fine search, then the word passed to the recogmtion and NLI modules This coarse to nanow search is very helpful in reducing the processing involved in the localization For example, such localization is very computauonally intense since the localization must transform the energy into the frequency domain and back Thus, by eliminating many putative hits in the coarse search, the processing is reduced If the SR module identifies the putative hit as an attenuon word, is passed to the natural language interface module 222 to be analyzed to determine which attenUon word has been uttered Note that the context of the natural language interface module is miually of attention words, 1 e , the system is searching for attention words to acUvate the system Once an attention word is found, the context of the NLICS is caused to change to a request context, such that it will be looking for requests constrained by the devices coupled to the NLICS The secondary cache of the base unit 106 is used to store secondary models, grammars and or lexicons for use in the remote umt 104 This compliments the speech recogmtion module which is designed to read in (stream) speech models and grammars from a secondary storage device or secondary cache (e g hard disk, CDROM, DVD) at run-time Once the data has been read in, it can immediately be used without any kind of preprocessing This effecuvely Ues in well with the idea of context switching In addition to the benefits of low processing requirements and the high speech recogmtion accuracy that comes with the grammar context-switching feature, the memory requirements are greatly reduced, since less frequently used grammars, etc may be stored in the secondary cache 310 and read hen required without occupying memory within die remote umt 104 Further, more acoustic data can be used which improves speech recogniuon accuracy, and vaπous approaches to speaker adaptation can be efficiently implemented as secondary storage devices can hold large amounts of base models for different dialects and accents Furthermore, the secondary cache may be a storage for models, grammars, etc that are downloaded from an external network 116 Refernng next to FIG 4, a flowchart is shown for the steps performed in the natU al language interface algoπthm of the namral language interface control system of FIGS 1 through 3 Iniually, the speech recognition module 204 and the namral language interface module 222 are initialized to the context of looking for attention words (Step 402) This allows the NLICS to accept non-prompted user requests, but first the system must be told that a user request is coming The attention word accomplishes this As such, the grammars and the models for the hidden Markov models are used to specifically identify the presence of an attenuon word Next, the remote umt receives the acousuc speech data at the microphone array (Step 404) The acoustic data is segregated into 12 8 msec frames using a 50% overlap A 38-dιmensιonal feature vector is deπved from the acousuc data These features consist of Mel-Frequency Cepstral coefficients 1-12 and the first and second order deπvaϋves of MFC coefficients 0-12 Thus, feature vectors are created from the acoustic data (Step 406) This is performed at die feature extracuon module 202 Next, the speech recogmtion module 204 applies acoustic hidden Markov models (HMM) and an N-gram grammar to the incoming feature vectors (as specified by the natural language interface) to deπve an m-vocabulary (IV) Viterbi (likelihood) score (Step 408) Then, the feature data is reprocessed using models of OOV events, e g , an ergodic bank of onophone models, to deπve an out-of-vocabulary (OOV) Viterbi score (Step 410) The garbage score is calculated from the IV an OOV scores, e g , the garbage score equals [Ln(IV score)-Ln(OOV score)]/number of frames (Block 411) A low score indicates a garbage utterance The N-best transcπbed text stπng(s) and conesponding garbage score(s) are passed to the natural language interface module 222 (Step 412) The namral language interface module 222 parses die incoming stπng(s) using a set of probabilistic context-free grammar (PCFG) rules as well as device context informauon for an attention utterance (Step 414) As descπbed above, the natural language interface module 222 requires an attention strategy, e g , the receipt of an attenuon word (I e , Mona) that is unique to the user, or speaker idenuficauon coupled with allowable grammar rules Once the user has the system's attention, l e , the namral language interface module 222 has detected an attenuon word (Step 416), the natural language interface module knows the user's identity It proceeds by configunng the system according to the user It does this by changing the relevant s> stem parameters and by direcung the speech recogmtion module 204 to change grammars to those appropπate for accepting commands and requests and according to the user The speech recogmuon module 204 changes lexicons according to the grammar rules and the individual user Thus, the speech recogmtion module 204 and the namral language interface module 222 change contexts to look for user requests (Step 418) Additionally, the natural language interface module directs the microphone anay of the base unit or base station to nanow its focus in order to better discπnunate against environmental noise Furthermore, if there are devices under NLICS control (TV, CD, etc ) which are playing at a high volume, the natural language interface module directs the amplifier to reduce its volume Then, the natural language interface module 222 initiates a timer and waits for the user's request until the time-out peπod has expired If the system times-out, the natural language interface module 222 reconfigures the system by resetting the relevant speech recognition module rules and lexicon to search for attention words Also, the microphone array and the amplifier volume are reset if they had been adjusted These resetung steps are such as those performed in Step 402 After switching to the context of looking for a user request (Step 418), Steps 404 through 414 are repeated, except that in this pass the acousuc speech represents a user request to operate one or more of the controlled devices If the namral language interface module 222 detects a user request (Step 416), i e a user request (as determined by the PCFG grammar system and device context) is received, it draws on of three conclusions (Steps 420, 422 or 424) According to Step 420, the user request is unambiguously understood and the natural language interface module can comply with a user request Thus, the namral language interface module 222 cames out the command by sending the appropnate signals via the device interface 210, as indicated by the device abstracuon Then, die context of the speech recogmtion module 204 and the natural language interface module 206 is switched back to look for attention words (Step 426), before proceeding to Step 404 According to Step 422, the user request is unambiguously understood and the natural language interface module cannot comply with the user request As such, the user is informed of this conclusion and prompts for further direcuon The system then waits for further user requests or umes out and proceeds to Step 426 According to Step 424, the ambigmty cannot be resolved for the request, in which case, die namral language interface module 222 requests clarification from the user, e g , by using the feedback module 228 and the speaker 214 The ambigmty is resolved according to the kind of ambigmty encountered If the ambiguity anses due to a low confidence, it affirms its conclusion with the user (e g , "Did you mean play the CD player?") If the user confirms the conclusion, the command is earned out, and the system is reset (Step 426) The system adjusts the user's profile to reflect the confidence with which a decision was made, as well as preference given a set of alternatives In some embodiments, over time, these statistics are used to reorder the PCFG rules and entπes in the relevant lexιcon(s) This results in a faster, more accurate system, since the most likely entnes will always be checked earlier and these more likely entπes will produce a higher confidence If the ambigmty anses due to a set of choices, it presents these alternatives to the user (e g , "Did you want to watch a movie on the DVD player or the VCR?") If the user selects from among the options provided, the natural language interface module 222 carπes out the command, otherwise the system is reset (Step 426) In either case, the user profile is updated as descπbed above If the ambiguity anses because of die cunent context (e g , the user requests to stop the TV and it is off), the user is made aware of this While the invention herein disclosed has been descπbed by means of specific embodiments and applications thereof, numerous modifications and vanations could be made thereto by those skilled in the art without departing from the scope of the invention set forth in the claims

Claims: CLAIMS What is claimed is 1 A natural language interface control system for operating a plurality of devices compnsing a first microphone array, a feature extracuon module coupled to the first microphone array, a speech recogmtion module coupled to the feature extraction module, wherein the speech recogmuon module utilizes hidden Markov models, a natural language interface module coupled to the speech recogmtion module, and a device interface coupled to the natural language interface module, wherein the namral language interface module is for operating a plurality of devices coupled to the device interface based upon non-prompted, open-ended namral language requests from a user 2 The system of Claim 1 further compnsing the plurality of devices coupled to the natural language interface module 3 The system of Claim 1 wherein the speech recogmtion module utilizes an N gram grammar 4 The system of Claim 1 wherein the natural language interface module utilizes a probabilistic context free grammar 5 The system of Claim 1 wherein the microphone array compπses a 3 dimensional microphone array compnsing a planar microphone array and at least one linear microphone array located in a different plane in space 6 The system of Claim 1 wherein the natural language interface abstracts each of the plurality of devices into a respective one of a plurality of grammars and a respective one of a plurality of lexicons conesponding to each of the plurality of devices 7 The system of Claim 1 wherein the natural language interface module searches for the non-prompted, open-ended user requests upon the receipt and recogmtion of an attention word 8 The system of Claim 1 wherein the namral language interface module context switches grammars, acoustic models, and lexicons upon receipt and recognition of an attenuon word 9 The system of Claim 1 further compnsing a grammar module for stoπng different grammars for each of the plurality of devices 10 The system of Claim 1 further compnsing an acoustic model module for stoπng different acoustic models for each of the plurality of devices 11 The system of Claim 1 wherein the device interface compπses a wireless device interface 12 The system of Claim 1 further compnsing an external network interface coupled to the natural language interface control system 13 The system of Claim 1 further compnsing a remote unit containing the first microphone ana> , the feature extraction module, the speech recognition module, and the natural language interface module 14 The system of Claim 13 further compnsing a base unit coupled to the remote umt 15 The system of Claim 14 wherein the base unit includes a second microphone anay 16 The system of Claim 15 wherein the microphone array and the second microphone array implement a 3 dimensional microphone array 17 A method of speech recogmtion compnsing searching for an attenUon word based on a first context including a first set of models, grammars, and lexicons, and switching, upon finding the attenuon word, to a second context to search for an open-ended user request, wherein second context includes a second set of models, grammars, and lexicons 18 A method of namral language control of one or more devices compnsing receiving an attention word, wherein the attention word indicates that an open- ended, natural language user request will be received, receiving the open-ended, namral language user request, matching the open-ended namral language request with the most likely command conesponding the open-ended namral language request, and transmitting the command to a respective one of the one or more devices 19 The method of Claim 18 further compnsing switching, after receiving the attenUon word, to a context for receiving open-ended namral language requests 20 The method of Claim 18 further compnsing applying speech recogmtion to the received attention word and the received open-ended, natural language user request 21 The method of Claim 20 wherein the applying the speech recogmtion further compπses applying hidden Markov models to the open-ended, namral language user requests 22 The method of Claim 18 wherein the matching compnses parsing the open- ended, namral language user request using probabilisuc context free grammar (PCFG) rules 23 The method of Claim 22 determining which of the one or more devices the open-ended, natural language user request is directed to 24 The method of Claim 23 further compnsing switching the context depending on the respective one of the one or more devices the open-ended, natural language user request is directed to 25 A namral language control system for controlling one or more devices compnsing means for receiving an attention word, wherein the attention word indicates that an open-ended, namral language user request will be received, means for receiving the open-ended, namral language user request, means for matching the open-ended natural language request with the most likely command conesponding the open-ended namral language request, and means for transmitting the command to a respective one of die one or more devices

Inventors: ALMSTRAND LARS CRISTIAN, KONOPKA COURTNEY CHARLES, ALMSTRAND, LARS, CRISTIAN, KONOPKA, COURTNEY, CHARLES
Applicants: SONY ELECTRONICS INC, SONY ELECTRONICS INC.
Application year: 2000
Main classification: G10L15
IPC codes: G06F17, G10L11, G10L15