Patent Document

CLAIM OF PRIORITY UNDER 35 U.S.C. §119 
     The present application claims the benefit of U.S. Provisional Patent Application Ser. No. 61/327,228, filed Apr. 23, 2010, which application is incorporated herein by reference as if set out in full. 
    
    
     CLAIM OF PRIORITY UNDER 35 U.S.C. §120 
     None. 
     REFERENCE TO CO-PENDING APPLICATIONS FOR PATENT 
     None. 
     BACKGROUND 
     1. Field 
     The technology of the present application relates generally to speech-to-text conversion for dictation systems, and more specifically, to methods and systems to train user profiles associated with speech recognition engines and test the trained profile to inhibit training a profile in a manner that may reduce accuracy. 
     2. Background 
     Many companies provide customers the ability to contact the company using a call center to field customer calls, correct customer problems, or direct the customer to the appropriate resource to solve the problems that initiated the call, real or imagined. Conventionally, a call center operates by a call being directed from a customer to an available agent or representative. Along with the telephone call, the agent or representative typically has a customer relation management screen that the company has authorized or specifically designed to facilitate assisting the customer. 
     Referring now to  FIG. 1 , a conceptual representation of the systems within a call center  100  is shown. The call center  100  includes both voice technologies which use the signaling and audio path and terminate at the agent&#39;s phone (or headset) and IP-based technologies that support the customer relationship management (CRM) application, whose graphical user interface (GUI) runs on the agent&#39;s processor, such as, for example, a personal computer or the like. To support this, the call center  100  includes, an automatic call distributor (ACD)  102  having an audio connection  104  to an agent phone  106 . ACD  102  also has an audio connection  108  to an interactive voice response unit (IVR)  110 . Audio connection  104  and  108  may be overlapping, completely separate, or a combination thereof. IVR  110  has a data connection  112  to CTI  114 . Computer/Telephony Interface (CTI)  114  typically provides call control  116  to ACD  102  and data and application control  118  to an agent&#39;s computer  120 . Thus, when a customer uses a telephone  122  or the like to call the call center over a conventional network  124 , such as the public switch telephone network (PSTN) shown, the audio, data, and applications necessary for the agent to assist the caller are provided. 
     While  FIG. 1  identifies a customer calling over a conventional PSTN as shown, calls from customers may originate from a computer or cable based voice over internet protocol (VoIP) network instead. The network  124  may be a conventional PSTN network as shown, such as, for example, when the customer is using a conventional landline or cellular telephone. Alternatively, network  124  may be a computing network, such as, for example, a LAN, a WAN, a WLAN, a WWAN, a WiFi, the internet, an Ethernet, or other private area network. When network  124  is a computing network, the call from the customer may originate from a VoIP enabled device, such as, for example, a computer telephone. Notice, VoIP telephones may be transferred to conventional PSTN networks using conventional technology. Moreover, conventional landlines, for example, may be connected to a computer network using a soft phone or media gateway. 
     Once the call between the customer service representative is established, and the CRM application is running on the representative&#39;s user interface, the customer service representative (CSR) would solicit input from the customer. Such input may consist of information such as, customer name, address, nature of the problem, and the like. Traditionally, the representative inputs this information by typing the information into the respective fields for input. At the end of the call, often the customer service representative would fill out a field in the CRM application generically known as notes or end of call notes. This field would typically be typed by the representative to acknowledge information such as, for example, the disposition of the customer complaint or the like. 
     To facilitate entering data into the CRM application, including entering data regarding the end of notes field, it may be possible to use a dictation system having a speech to text engine or speech recognition engine to allow the data to be dictated or captured during normal conversation/dialogue between the agent and the customer. See, for example, U.S. Pat. No. 7,702,093, issued Apr. 20, 2010, and incorporated herein by reference as if set out in full, and U.S. patent application Ser. No. 12/694,115, filed Jan. 26, 2010, and incorporated by reference as if set out in full. The audio signal would be directed to the speech recognition engine that would return textual data for input to the appropriate fields. Speech recognition engines may generally be classified as a natural language recognizer, such as DRAGON® NaturallySpeaking® currently available from Nuance Communications, Inc., or as a grammar-based or pattern matching recognizer, such as the Nuance Recognizer V9 also available from Nuance Communications, Inc. 
     Speech recognition engines, and in particular, natural language recognizers, may require training to function properly. Traditionally, training is accomplished by the user when the user edits the transcribed text. Thus, if the user speaks the word “potato” but the transcription returns the word “tomato,” the user can correct the transcription and this correction is used to train the system. Ideally, once trained, the next time the user speaks the word “potato”, the transcription would return the word “potato.” 
     Using dictation as a tool to add information to fields in a CRM application, however, to date have been cumbersome and unwieldy. Speech recognition engines that require training to function properly are difficult and expensive as the customer representative must take valuable time to train the speech recognition engine such that it functions properly. Moreover, it has been discovered that in some cases, corrections to transcriptions when used to train the grammar based system, in fact, result in reduced accuracy of the overall system. Thus, against this background, it would be desirous to provide improved training and feedback for dictation based systems as well as testing for increased, or at least no decrease, in system accuracy based on the training. 
     SUMMARY 
     Embodiments disclosed herein address the above stated needs by providing scheduled training using actual audio samples and testing of trained speech recognition engines to determine whether the training increases or decreases accuracy. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram of a call center system; 
         FIG. 2  is a functional block diagram of a training system consistent with an exemplary embodiment of the technology of the present application; 
         FIG. 3  is an exemplary operational flowchart illustrating an exemplary methodology associated with an embodiment of the technology of the present application; 
         FIG. 4  is an exemplary display of a training screen associated with an exemplary embodiment of the technology of the present application; and 
         FIG. 5  is an exemplary operational flowchart illustrating an exemplary methodology associated with an embodiment of the technology of the present application. 
     
    
    
     DETAILED DESCRIPTION 
     The technology of the present application will now be explained with reference to a customer call center application. The technology, in general, is described as directing the audio from a user to a remote server that converts the audio to text and returns a transcription of the text. One of ordinary skill in the art on reading the disclosure will now recognize that the technology of the present application will be useful in other dictation systems that use training. For example, instead of a remote server to provide the transcription, the transcription may be loaded directly to the user processor. Additionally, the technology of the present application may be used in systems other than call centers to provide training for speech recognition engines relating to dictation systems. For example, both medical and legal services frequently use dictation systems. Moreover, the technology of the present application will be described with relation to exemplary embodiments. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments. Additionally, unless specifically identified otherwise, all embodiments described herein should be considered exemplary. 
     As explained above, dictation based speech-to-text conversion software has existed for some time. The dictation may be performed on a local processor such that real-time or near real-time transcription of the dictation may be provided to the user. Instead of having the dictation performed on a local processor, it also is possible to stream the audio to a remote server that performs the dictation and returns textual data to the local processor. Alternatively, the dictation may be batch loaded to a central processor or server where the transcription is returned at a later date, returned shortly after the dictation, or stored for later retrieval. Either dictation system may use a speech recognition engine that is either a natural language recognizer, as a grammar-based or pattern matching recognizer, or a combination thereof. The technology of the present application, however, will be described in relation to natural language recognizer as they provide a more robust ability to generate data outside of a pattern match system. 
     Referring first to  FIG. 2 , a functional diagram of a training system  200  for the technology of the present application is provided. Training system  200  includes a processor  202 , such as, for example, a microprocessor, a central processing unit, a desktop computer, a server, a laptop computer, a handheld computer or the like. Processor  202  controls the major functions of the training system  200  including the functional operations described herein below. Processor  202  also processes the various inputs and/or data that may be required to operate training system  200 . A memory  204  is interconnected with processor  202 . Memory  204  may be remotely located or co-located with processor  202 . The memory  204  also may store data necessary or convenient for operation of the training system  200  as will be explained herein. As will be explained, the memory  204  may include memory to store audio and text files to generate corrections and training material as will be explained further below. Training system  200  further includes an audio reception interface  206  or port that can receive or transmit audio information from and to a user, such as, for example, a customer service representative or transcription technician. Training system  200  also includes a data interface  208  or port to receive or transmit data from and to the user. 
     Interconnected to processor  202  is a speech recognition or speech-to-text engine  210  that converts the audio signal received from the user into a text file that can be returned to the user or further processed as part of the transcription. Speech recognition engine  210  is generally understood in the art and will not be further explained herein. Engine  210  may be provided remote from, integrated with, or co-located with processor  202 . 
     Training system  200  further includes output devices  212 , such as, a display, a printer, an email generator, or the like as is conventional in the art to output the results of the training system  200 . To facilitate training of the speech recognition engine, as will be explained further below, output device  212  may comprise a speaker and a display. The speaker would play audio files, which audio files may be stored in memory  202 , and the display would display the associated transcription or text file of the audio, which also may be stored in memory  202 . Training system  200  may further comprise input devices  214 . Input device  214  may include any conventional device, but will be described using reference to a conventional keyboard for convenience. Output device  212  and input devices  214  may be co-located or remote from training system  200 . In such cases, the audio and text files may be transmitted to a remote location using a conventional or private network connection. 
     While shown and described as performing the transcription using speech recognition engine  210 , training system  200  may receive a text file and audio file from a remotely located transcription engine or memory. 
     Using dictation based systems for various industries, such as, for example, call center applications, medical dictation, legal dictation, and the like, requires that the transcription system be trained to recognize a user&#39;s dialectic; specialized words, clauses and phrases; and the like. Conventionally, training for these systems involve, for example, time that a customer service representative, doctor, lawyer, or the like may otherwise spend assisting customers, patients, or other clients. In order to improve efficiency, it would be desirable to provide alternative ways to train the speech recognition engine or speech-to-text engine  210 . Referring now to  FIG. 3 , an exemplary flowchart  300  is provided showing exemplary operational steps to training a speech recognition engine where the customer service representative does not need to take time away from assisting customers. 
     It is assumed that an audio file  216  and a text file  218  of the transcribed audio are available in memory  204 . However, if not, audio file  216  and text file  218  may be transmitted to training system  200 , step  302 . An audio file is stored in memory  204  as audio file  216 , step  304 . If the text file is not provided, speech recognition engine  210  transcribes the audio file, step  306 . Speech recognition engine  210  may be co-located with training system  200 , integrated with training system  200 , or remote from training system  200  as a matter of design choice. Speech recognition engine  210  may transcribe the audio file substantially in real time or near real time, but real time or near real time transcription is not necessary. Once the transcription is complete, the text file is returned to the customer service representative, step  308 , and stored in memory  204  as text file  218 , step  310 . If speech recognition engine is remote from training system  200 , the text file is received by processor  202  via data port  208  and stored in memory  204 . Audio file  216  and text file  218  may be associated via a link  219  or the like. Link  219  may include, for example, a hyperlink, a mapping, a correlation in a database, or the like. To train the speech recognition engine  210 , a training user would initiate the training sequence, step  312 . Processor  202  would fetch from memory  204  the audio file  216  and the associated or linked text file  218 , step  314 . The processor would display the text file  218  to the training user on, for example, a display  400  shown in  FIG. 4 , step  316 . In this case, display  400  includes an original transcription text display  402 . Processor  202  would substantially simultaneously, or on a prompt such as by clicking play icon  410 , play audio file  216 , step  318 . The training user would make corrections either to the original transcription text display  402  or to a correction display  406  based on comparing the audio to the transcription text, step  320 . In this case, changes are shown by corrections  412 . Once the corrections are inputted, the training user would click the train icon  408  or the like, step  322 , and speech recognition engine  210  would train based on the noted changes in a conventional fashion which will not be further explained herein, step  324 . 
     The training of speech recognition engine  210  may occur substantially in real time after the training icon  408  is clicked. Alternatively, the training information may be stored in memory  204  or transmitted to a remote memory networked to training system  200  and/or speech-recognition engine  210  such that the training of speech recognition engine may be accomplished subsequently. In particular, the actual training of speech-recognition engine  210  may be delayed to process the training in a conventional manner at a time when the processor has more available resources. Thus, an administrator may schedule the training of speech-recognition engine  210  for particular users to occur at particular times, or the like when the speech-recognition engine is considered to be less “busy”. Alternatively, the training system or the speech-recognition engine may monitor the speech-recognition engine for available capacity and automatically prompt training if certain predetermined conditions are satisfied, such as, for example, the processor load is below a certain percentage of capacity, the particular time (whatever that may be) is considered time when training is allowed, etc. For example, there may be times when processor load is estimated to be relatively high, but for one reason or another at any particular moment, the processor load is relatively low. Because demand on the processor may increase during these expected high load times, the training system would be prohibited from initiating training even if processor load happens to be low. 
     As mentioned above, it has been discovered that in certain instances training of the speech-recognition engine decreases the accuracy of the engine such that the confidence of the transcription is diminished. Training is important to increase the accuracy and confidence associated with transcriptions. So training should be undertaken whenever warranted. However, it is not currently possible to predict whether any training of the speech recognition engine will result in increased confidence of the transcriptions, decreased accuracy of the transcriptions, or no change in the confidence of the transcriptions. Referring now to  FIG. 5 , an exemplary flowchart  500  is provided showing exemplary operational steps to training a user profile associated with a speech recognition engine where the trained user profile is tested for increased, or at least not decreased, confidence, accuracy, or other measure, prior to implementing the trained user profile. A customer service representative does not need to take time away from assisting customers. To initiate the process, the training system receives the training information, step  502 . The training information may be from a pre-programmed training session, training information from step  324  of flowchart  300 , or other applicable training information. The user profile to be trained is fetched from memory, step  504 . The user profile to be trained is referred to below as the initial user profile. Optionally, the initial user profile prior to training is at least temporarily stored as the initial user profile in the memory, step  506 . The initial user profile is trained using the received training information in a conventional manner, step  508 , that will not be further explained herein, but such training is available via DRAGON® NATURALLYSPEAKING® from Nuance, Inc. The user profile after training, hereinafter the interim user profile, optionally is at least temporarily stored, step  510 . Next, the interim user profile is used by the speech recognition engine to transcribe an audio file, step  512 . The audio file may, for example, be the same audio file from which the training information was received. Alternatively, the audio file may be a customer test audio file, a previously transcribed audio file, or a new audio file. The accuracy of the transcription using the interim user profile is determined, step  514 . Ideally, the audio file would be a known audio file such that the accuracy of the transcription using the interim user profile may be measured automatically. Notice, while accuracy is the performance metric called out herein, other performance metrics may be used. For example, the time delay between receiving the audio and returning a transcription may be used as a performance metric, average confidence of transcribed words may be used as a performance metric, etc. The accuracy of the interim user profile is compared to an accuracy of the initial user profile, step  516 . For example, if the audio file from which the training information was provided is the audio file used for test purposes, the accuracy of the initial user profile can be determined as the accuracy was available via the corrections associated with flowchart  300 . Alternatively, the initial user profile would be used to transcribe, for example, a customer test audio file, or the like. Next, it is determined if the accuracy of the interim user profile is less than the accuracy of the initial user profile, step  518 . If it is determined that the accuracy of the interim user profile is less than the accuracy of the initial user profile, then the interim user profile is discarded and the initial user profile is stored as the user profile or the final user profile, step  520 . Otherwise, the interim user profile, which is the trained profile, is stored as the user profile or the final user profile, step  522 . 
     As can be appreciated, the above method and system provides a system for training where the customer service representative may continue to assist customers without the need to train the dictation system. 
     Those of skill in the art would understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof. 
     Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. 
     The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. 
     The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), flash memory, Read Only Memory (ROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal. 
     The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Technology Category: g