Patent Publication Number: US-2006004570-A1

Title: Transcribing speech data with dialog context and/or recognition alternative information

Description:
BACKGROUND OF THE INVENTION  
      The present invention relates to speech recognition. More particularly, the present invention relates to transcribing speech data used in the development of such systems.  
      Speech recognition systems are increasingly being used by companies and organizations to reduce cost, improve customer service and/or automate tasks completely or in part. For example, speech recognition systems can be employed to handle telephone calls by prompting the caller to provide a person&#39;s name or department, receive a spoken utterance, perform recognition, compare the recognized results with an internal database, and to transfer the call.  
      Generally, a speech recognition system uses various modules, such as an acoustic model and a language model as is well known in the art, to process the input utterance. Both general purpose models, or application specific models can be used, if, for instance, the application is well-defined. In many cases though, tuning of the speech recognition system, and more particularly, adjustment of the models is necessary to ensure that the speech recognition system functions effectively for the user group that it is intended. Once the system is deployed, it may be very helpful to capture, transcribe and analyze real spoken utterances in order that the speech recognition system can be tuned for optimal performance. For instance, language model tuning can increase the coverage of the system, while removing unnecessary words so as to improve system response and accuracy. Likewise, acoustic model tuning focuses on conducting experiments to determine improvement in search, confidence and acoustic parameters to increase accuracy and/or speed of the speech recognition system.  
      As indicated above, transcription of recorded speech data collected from the field provides a means for evaluating system performance and to train data modules. Literally, current practices require a data transcriber/operator to listen to utterances and then type or otherwise associate a transcription of the utterance for each utterance. For instance, in a call transfer system, the utterances can be names of individuals or departments the caller is trying to reach. The transcriber would listen to each utterance and transcribe each request, possibly by accessing a list of known names. Transcription is time consuming and thus, an expensive process. In addition, transcription is also error-prone, particularly for utterances comprising less common names or names with foreign origins. Nevertheless, transcription data is very helpful for speech recognition development and deployment.  
      There is thus an on-going need for improvements in transcribing speech data. A method or system that addresses one, some or all of the foregoing shortcomings would be particularly useful.  
     SUMMARY OF THE INVENTION  
      Methods and modules for easy and accurate transcription of speech data are provided. Utterances related to a single task are grouped together and processed using combinations of associated sets of recognition results and/or context information in a manner that allows the same transcription for a selected recognition result to be assigned to each of the utterances under consideration. In this manner, the process of speech data transcription is converted into an accurate and easy data verification solution.  
      In further embodiments, selection of the single recognition result includes removing from consideration at least one of the recognition results based on the context information. For example, this can include removing from consideration those recognition results that have been proffered to the user, but rejected as being incorrect. Likewise, if the user confirms that a recognition result is correct in the context information, the corresponding recognition result can be assigned to all other similar utterances  
      In yet a further embodiment, measures of confidence can be assigned or associated explicitly or implicitly with the single selected recognition result based on the context information and/or based on the presence of the single selected recognition result in the set of recognition results. The measure of confidence allows for a qualitative or quantitative indication as to whether the transcription provided for the utterance is correct. For instance, the measure of confidence allows the user of transcription data to evaluate performance of a speech recognition system under consideration or tune the data modules based on only transcription data having a selected level of confidence or greater. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       FIG. 1  is a block diagram of a general computing environment in which the present invention may be practiced.  
       FIG. 2  is a block diagram of a system for processing speech data.  
       FIG. 3  is a flow diagram for a first method of processing speech data.  
       FIG. 4  is a flow diagram for a second method of processing speech data.  
       FIG. 5  is a flow diagram for a third method of processing speech data. 
    
    
     DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS  
      The present invention relates to a system and method for transcribing speech data. However, prior to discussing the present invention in greater detail, one illustrative environment in which the present invention can be used will be discussed first.  
       FIG. 1  illustrates an example of a suitable computing system environment  100  on which the invention may be implemented. The computing system environment  100  is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the computing environment  100  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  100 .  
      The invention is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.  
      The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Those skilled in the art can implement the description and/or figures herein as computer-executable instructions, which can be embodied on any form of computer readable media discussed below.  
      The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both locale and remote computer storage media including memory storage devices.  
      With reference to  FIG. 1 , an exemplary system for implementing the invention includes a general purpose computing device in the form of a computer  110 . Components of computer  110  may include, but are not limited to, a processing unit  120 , a system memory  130 , and a system bus  121  that couples various system components including the system memory to the processing unit  120 . The system bus  121  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a locale bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) locale bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus.  
      Computer  110  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  110  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  100 . Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier WAV or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, FR, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media.  
      The system memory  130  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  131  and random access memory (RAM)  132 . A basic input/output system  133  (BIOS), containing the basic routines that help to transfer information between elements within computer  110 , such as during start-up, is typically stored in ROM  131 . RAM  132  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  120 . By way ◯ example, and not limitation,  FIG. 1  illustrates operating system  134 , application programs  135 , other program modules  136 , and program data  137 .  
      The computer  110  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG. 1  illustrates a hard disk drive  141  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  151  that reads from or writes to a removable, nonvolatile magnetic disk  152 , and an optical disk drive  155  that reads from or writes to a removable, nonvolatile optical disk  156  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  141  is typically connected to the system bus  121  through a non-removable memory interface such as interface  140 , and magnetic disk drive  151  and optical disk drive  155  are typically connected to the system bus  121  by a removable memory interface, such as interface  150 .  
      The drives and their associated computer storage media discussed above and illustrated in  FIG. 1 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  110 . In  FIG. 1 , for example, hard disk drive  141  is illustrated as storing operating system  144 , application programs  145 , other program modules  146 , and program data  147 . Note that these components can either be the same as or different from operating system  134 , application programs  135 , other program modules  136 , and program data  137 . Operating system  144 , application programs  145 , other program modules  146 , and program data  147  are given different numbers here to illustrate that, at a minimum, they are different copies.  
      A user may enter commands and information into the computer  110  through input devices such as a keyboard  162 , a microphone  163 , and a pointing device  161 , such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  120  through a user input interface  160  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor  191  or other type of display device is also connected to the system bus  121  via an interface, such as a video interface  190 . In addition to the monitor, computers may also include other peripheral output devices such as speakers  197  and printer  196 , which may be connected through an output peripheral interface  190 .  
      The computer  110  may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer  180 . The remote computer  180  may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  110 . The logical connections depicted in  FIG. 1  include a locale area network (LAN)  171  and a wide area network (WAN)  173 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.  
      When used in a LAN networking environment, the computer  110  is connected to the LAN  171  through a network interface or adapter  170 . When used in a WAN networking environment, the computer  110  typically includes a modem  172  or other means for establishing communications over the WAN  173 , such as the Internet. The modem  172 , which may be internal or external, may be connected to the system bus  121  via the user-input interface  160 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  110 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 1  illustrates remote application programs  185  as residing on remote computer  180 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.  
      It should be noted that the present invention can be carried out on a computer system such as that described with respect to  FIG. 1 . However, the present invention can be carried out on a server, a computer devoted to message handling, or on a distributed system in which different portions of the present invention are carried out on different parts of the distributed computing system.  
      As indicated above, the present invention relates to a system and method for transcribing speech data, which can be used for instance, to further train a speech recognition system or evaluate performance. Resources used to perform transcription include speech data indicated at  200  in  FIG. 2 , which corresponds to utterances to be transcribed. The speech data  200  can be actual waveform data corresponding to recorded utterances, although it should be understood that speech data  200  can take other forms such as but not limited to acoustic parameters representative of spoken utterances.  
      A second resource for performing transcription include sets of recognition results  204  from a speech recognition system. In particular, a set of recognition results is provided or associated with each utterance to be transcribed in speech data  200 . In general, each set of recognition results is a at least a partial list of possible or alternative transcriptions of the corresponding utterance. Commonly, such information is referred to as an “N-Best” list that is generated by the speech recognition system based on stored data models such as an acoustic model and a language model. The N-Best list entries can have associated confidence scores used by the speech recognition system in order to assess relative strengths of the recognition results in each set, where the speech recognition system generally chooses the recognition result with the highest confidence score. In  FIG. 2 , the sets of recognition results are illustrated separately from the speech data  200  for purposes of understanding. Each set of recognition results is closely associated with the corresponding utterance, for example, even stored together therewith. It should also be noted that these sets of recognition results  204  can also be generated when desired by simply providing the utterance or speech data to a speech recognition system (preferably of the same form from which the speech data  200  was obtained), and obtaining therefrom a corresponding set of recognition results. In this manner, the number of recognition results for a given utterance in each set can be expanded or reduced as necessary during the transcription procedure described more fully below.  
      A third resource that can be accessed and used for transcription is information related to the context for at least one, and preferable, a set of utterances related to performing a single task. The context information is illustrated at  206  in  FIG. 2 . For instance, a set of utterances in speech data  202  can be for a single caller in a speech recognition call transfer application who has had to provide the desired recipient&#39;s name a number of times. For example, suppose the following dialog occurred between the speech recognition system and the caller:  
      System: “Who would you like to reach?” 
      Caller: “Paul Toman” 
      System: “Did you say Paul Coleman?” 
      Caller: “No, Paul-Toman” 
      System: “Did you say Paul Toman?” 
      Caller: “Yes” 
      In this example, the caller provided “Paul Toman” twice, in addition to a correction “No” as well as confirmation “Yes”. Depending on the dialog between the speech recognition system and the caller, context information  206  can include similar utterances related to performing a single desired task, and/or correction information and/or confirmation information as illustrated above. In addition, the context information can take other forms such as spelling portions or complete words in order to perform the task, and/or providing other information such as e-mail aliases in order to perform the desired task. Likewise, context information can take other forms besides spoken utterances such as data input from a keyboard or other input device as well as DTMF tones generated from a phone system as but just another example.  
      Speech data  200 , sets of recognition results  204  and/or context information  206  are provided to a transcription module  208  that can process combinations of the foregoing information and provide transcription output data  210  according to aspects of the present invention.  FIG. 3  illustrates a first method  300  for processing just the speech data  202  and corresponding sets of recognition results  204  in order to provide transcription output data  210 . Method  300  includes step  302  comprising receiving or identifying as a group speech data corresponding to a set of similar utterances related to a single task as well as an associated set of recognition results for each of the utterances. At step  304 , having grouped the sets of similar utterances and the corresponding recognition results based on the single task, a single recognition result is selected from the grouped (whether in fact combined or not) sets of recognition results. Transcription data is then assigned at step  306  for each of the similar utterances based on the selected recognition result. In the context of the example provided above, there are two utterances for “Paul Toman” provided by the caller, each of these utterances would be assigned transcription data, commonly textual data or character sequences, indicative of “Paul Toman”.  
      The method of  FIG. 3  illustrates how speech data  200  and the sets of the recognition results  202  can be processed in order to provide transcription data for similar utterances. In one embodiment, the transcription module  208  can render the utterances to a transcriber, possibly in combination with rendering the sets of recognition results provided by the speech recognition system so that the transcriber can select the correct transcription for multiple occurrences of the same utterance, thereby quickly assigning transcription information to a set of similar utterances without individually having to select the transcription data separately for each utterance. In this manner, the transcriber can process the speech data quicker, thereby significantly saving time and improving efficiency.  
      In a further embodiment, step  302  can include receiving context information  206  of the utterances for the task, while the step of selecting the single recognition result is further based on the context information  206 . This is illustrated in  FIG. 4 . As indicated above, context information can take many different forms. Probably, the most definitive form, as illustrated above in the foregoing example, is when the caller informs the system a selected recognized result is correct. Thus, in response to the second utterance of the caller, the speech recognition system provided a set of recognition results (e.g. N-Best list) that presumably ranked “Paul Toman” as the best possibility for the utterance. Using the confirmed recognition result from the context information, the transcription module  208  can select this transcription and assign it to both of the utterances. It should be noted that little or any transcriber/operator interaction is necessary under this scenario since the transcription module  208  can assume that the selected recognition result is correct due to the confirmation in the dialogue between the system and the caller.  
      Even if the confirmation was not present as in the example provided above, additional context information can be used to efficiently select a single recognition result for the set of utterances. In one embodiment, this can include rendering each of the recognition results for each of the utterances to the transcriber/operator with the additional information learned from the context information. In the example above, the speech recognition system incorrectly selected “Paul Coleman” in response to the first utterance since the caller indicated that this name was incorrect by stating “No, Paul Toman.” The transcription module  208  can use this additional information (the fact that the selected recognition result was wrong) to modify the sets of recognition results in order to convey to the transcriber/operator that “Paul Coleman” was incorrect. For instance, the transcription module  208  could simply remove “Paul Coleman” from each of the sets of recognition results, or otherwise indicate that this name is incorrect. Thus, assuming that the affirmative confirmation “Yes” was not present in the above dialogue and only the two utterance providing the persons name were present (for instance, if the caller gave up after providing the person&#39;s name the second time), the transcriber/operator may easily select “Paul Toman” as the correct recognition result since this recognition result remains relatively high in each of the sets of recognition results. In further embodiments, the transcription module  208  could combine the sets of recognition results, based on, for example, confidence scores, in order to provide a single list based on all of the utterances. Again, this may allow the transcriber/operator to easily select the correct recognition result that will be assigned to all of the utterances spoken for the single task under consideration.  
      The manner in which recognition results are rendered to the transciber/operator can take numerous forms. For example, rendering can comprise rendering the recognition results for different utterances at the same time and before the step of selecting. While, in yet a different embodiment, rendering can comprise rendering the recognition results for different utterances successively in time with the rendering of the corresponding utterance.  
       FIG. 5  illustrates another method for processing speech data, which is operable by the transcription module  208 . As with the methods described above, method  500  includes receiving speech data  200  corresponding to a set of utterances related to a single task and context information  206  of the utterances for the single task at step  502 . At step  504 , the transcription module selects a single recognition result based on the context information  206 . At step  506 , the transcription module  208  assigns transcription data for each utterances based on the selected recognition result. In the dialogue scenario provided above, the transcription module  208  can easily ascertain the correct transcription for each of the utterance is “Paul Toman” due to the presence of the confirmation “Yes.” In this example, a set of recognition results for each of the utterances for the person&#39;s name is not really necessary because the confirmation is present in the dialogue. Thus, if the transcription module has the transcription for “Paul Toman”, for instance, from the set of recognition results for the second utterance, the transcription module  208  can assign the transcription “Paul Toman” to both of the utterances. As indicated above, context information can take other forms such as but not limited to context information having confirmations. Other examples, include dialog indicating a selection by the speech recognition system was wrong, partial or complete spellings of words, and/or additional information such as e-mail aliases, etc.  
      In addition to providing transcription data for each utterance based on the selected recognition result, a measure of confidence pertaining to whether the transcription provided for the utterance is correct can also be optionally provided. In the methods illustrated in  FIGS. 3-5 , the measure of confidence for each utterance can be included in steps  306  and  506 . The measure of confidence allows the user of the transcription output data  208  to evaluate performance of the speech recognition system under consideration or tune the data modules based on, for example, only transcription data  208  having a selected level of confidence or greater. In one embodiment, a measure of confidence can be ascertained quantitatively from the sets of recognition results and/or context information  206  related to each of the sets of utterances. For example, if the user has confirmed a recognition result in the dialogue, such as illustrated above, the transcription module can assign a “high” confidence measure to the transcription output data  208  for these utterances.  
      In another dialogue exchange, suppose the user did not confirm the recognition result from the speech recognition system for one of the utterances, but the selected recognition result and provided in transcription output  208  occurred in each of the sets of recognition results for the utterances under consideration. In other words, the selected recognition result occurred in each of the N-Best lists for each of the utterances. In this scenario, the transcription module  208  can assign a “medium-high” confidence level to the resulting transcription output data  208 .  
      In another dialogue exchange of utterances, suppose the transcriber/operator has chosen a recognition result that only appeared in one of the sets of recognition results, then transcription module  208  could assign a “medium-low” confidence level for the transcription output data.  
      Finally, suppose the transcriber/operator provided a recognition result that was not present in any of the sets of recognition results, or was a recognition result that was not ranked high in any of sets of recognition results, than the transcription module  208  could assign a confidence level of “low” to the corresponding transcription output data.  
      The foregoing are but some examples of criteria for assigning confidence measures to transcription output data. In general, the criteria can be based on the context information  206  and/or based on the set of recognition results such whether or not the selected recognition result appeared in one or all of the sets of recognition results, or its ranking in each of the sets of recognition results. Assignment of the confidence measure to the transcription data can be done explicitly or implicitly. In particular, each transcription in the transcription output data  208  could include an associated tag or other information indicating the corresponding confidence measure. In a further embodiment, explicit confidence levels may not be present in the transcription output data  208 , but rather, be implicit by merely forming the transcript output data into groups, where all the “high” confidence level transcription output data is grouped together, and all of the other levels of confidence measure for the transcription output data are likewise grouped together. In this manner, the user of the transcription output data  208  can simply use which ever collection of transcription output data  208  he/she desires.  
      In summary, the present invention provides a framework for easy and accurate transcription of speech data. Utterances related to a single task are grouped together and processed using combinations of associated sets of recognition results and/or context information in a manner that allows the same transcription for a selected recognition result to be assigned to each of the utterances under consideration. Aspects of the invention disclosed herein have converted the process of data transcribing into an accurate and easy data verification solution.  
      Although the present invention has been described with reference to particular embodiments, workers skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope of the invention.