Abstract:
Typical textual prediction of voice data employs a predefined implementation arrangement of a single or multiple prediction sources. Using a predefined implementation arrangement of the prediction sources may not provide a good prediction performance in a consistent manner with variations in voice data quality. Prediction performance may be improved by employing adaptive textual prediction. According to at least one embodiment determining a configuration of a plurality of prediction sources, used for textual interpretation of the voice data, is determined based at least in part on one or more features associated with the voice data or one or more a-priori interpretations of the voice data. A textual output prediction of the voice data is then generated using the plurality of prediction sources according to the determined configuration. Employing an adaptive configuration of the text prediction sources facilitates providing more accurate text transcripts of the voice data.

Description:
BACKGROUND OF THE INVENTION 
     Speech-to-text transcription is commonly used in many applications. The transcription is usually performed by a human agent. However, the use of human agents to transcribe voice data to text is costly, and sometimes the transcription quality is less than satisfactory. With significant advances in speech recognition and language modeling tools, machine-based solutions for speech-to-text transcription are becoming a reality. Such solutions may be used in combination with a human agent or separately. 
     SUMMARY OF THE INVENTION 
     According to at least one embodiment, a computerized method or a corresponding apparatus for performing adaptive textual prediction of voice data comprise: determining a configuration of a plurality of prediction sources, used for textual interpretation of the voice data, based at least in part on one or more features associated with the voice data or one or more a-priori interpretations of the voice data; and generating a textual output prediction of the voice data using the plurality of prediction sources according to the configuration determined. 
     The method further comprises extracting the one or more features associated with the voice data or the one or more a-priori interpretations of the voice data. The one or more features include a signal-to-noise ratio associated with the voice data, complexity measure of a lattice representing at least one a-priori interpretation of the voice data, or an a-priori interpretation of the voice message generated by a human agent. The multiple prediction sources include a language model module, lattice decoder module, or a human agent. The textual output prediction may be provided to a human agent to facilitate generating a final transcript of the voice data. Alternatively, the textual output prediction may be used as the final transcript of the voice data. 
     In determining the configuration of a plurality of prediction sources, an order according to which the multiple prediction sources are to be applied is determined, weightings associated with the multiple prediction sources are determined, or a subset of the plurality of prediction sources for use in generating the textual output prediction is determined. A representation of the determined configuration may be sent to another device or stored in a database. A database storing a representation of a previous configuration of the plurality of prediction sources may be updated based on the configuration determined. A representation of the determined configuration includes an indication of an order according to which the multiple prediction sources being applied, indication of weightings associated with the multiple prediction sources, or indication of a subset of the plurality of prediction sources for use in generating the textual output prediction. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing will be apparent from the following more particular description of example embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments of the present invention. 
         FIG. 1  is a block diagram of a system for speech-to-text transcription according to at least one example embodiment. 
         FIG. 2A  is a block diagram illustrating an example adaptation of prediction sources for text transcription of voice data. 
         FIG. 2B  is a block diagram illustrating another example adaptation of prediction sources for text transcription of voice data. 
         FIGS. 3A and 3B  show tables illustrating example weighting coefficients assigned to different prediction sources under different signal-to-noise ratios. 
         FIG. 4  is a flow chart illustrating a method of text prediction according to at least one example embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     A description of example embodiments of the invention follows. 
     In transcribing voice data into text, the use of human agents alone may be costly and of poor quality sometimes. Agents transcribing hours-long voice data may be under strict time constraints. The voice data may not always have good audio quality. Such factors may result in unsatisfactory transcription results. To address the issues of cost and quality in speech-to-text transcription, computer-based text prediction tools are employed. 
     Speech recognition applications known in the art may not provide text transcripts corresponding to input speech signals. Instead, an output of a speech recognition application may be in the form of statistical data illustrating different potential interpretations of a respective input speech signal. In addition, speech recognition applications may process a speech signal on a per-utterance or per-phoneme basis and may not consider linguistic rules or the context of the speech, or conversation, associated with the input speech signal. Therefore, the output of a speech recognition application is usually fed to a text prediction source to generate a text prediction corresponding to the input speech signal. A single source or multiple text prediction sources may be applied to a prior text interpretation, e.g., output of a speech recognition application or a transcript by an agent, of a speech signal. While the use of multiple prediction sources usually results in better performance than using a single prediction source, a single arrangement of how such multiple prediction sources are employed may not provide equally good performance under different conditions. In the following, different embodiments of adaptation of multiple text prediction sources are described. 
       FIG. 1  is a block diagram of a system  100  for speech-to-text transcription according to at least one example embodiment. In the system  100 , voice data  101 , e.g., a speech signal, is fed to a speech recognition module  110 . The speech recognition module  110  generates text data  102  as output. The generated text data  102  may be in the form of statistical data with probability values assigned to text words therein. In other words, the generated text data  102  may represent potential multiple interpretations of the voice data. The speech recognition module  110  may include one or more speech recognition applications. A text transcript  103  of the voice data may also be generated by a human agent through a first agent device  180 . The output of the speech recognition module  102 , the text transcript  103  generated by the agent, or both may be used by the adaptive text prediction module  120  to generate an output text prediction  109  of the respective voice data  101 . The output of the speech recognition module  102 , the text transcript  103  generated by the agent, or both may be viewed as a-priori text interpretation(s) of the corresponding voice data  101 . The output text prediction  109  provided by the adaptive text prediction module  120  may be provided to a second agent, associated with a second agent device  190 , to facilitate providing a final text transcript of the respective voice data  101 . Alternatively, the output text prediction  109  generated by the adaptive text prediction module  120  may be used as the final text transcript of the respective voice data  101 . 
     In generating the output text prediction  109 , the adaptive text prediction module  120  is configured to employ multiple text prediction sources or tools to the a-priori text interpretations. According to at least one example embodiment, the multiple prediction sources are employed according to adaptable configuration(s). Specifically, the adaptive text prediction module  120  includes an adaptation module  124  configured to determine a configuration of the multiple prediction sources based on features  105  associated with the voice data  101 , the text data  102  generated by the speech recognition module  110 , the text transcript  103 , or a combination thereof. The adaptive text prediction module  120  also includes an execution module  128  configured to execute the determined configuration of the multiple text prediction sources. 
     The features  105  may be provided to the adaptation module  124  by a feature extraction module  130 . The feature extraction module extracts the features  105  from voice data  101 , text data  102  generated by the speech recognition module  110 , text transcript  103  provided by the first agent, or a combination thereof. Examples of the features  105  include, for example, signal-to-noise ratio of the voice data  101 , characteristics of the speech recognition module output  102 , a measure of the accuracy or quality of text transcript  103 , or the like. 
     Based on the received features  105 , the adaptation module  124  determines the configuration of the multiple prediction sources. According to one scenario, the adaptation module  124  may analyze the features  105  to generate further parameters for use in determining the configuration of the multiple prediction sources. Alternatively, the adaptation module  124  may map the received features  105  to a particular configuration based on, for example, a mapping table. According to yet another scenario, the adaptation module  124  may rank or assign a priority value to each of the multiple text prediction sources, based on the received features  105 , and then determine a configuration based on the ranking or priority values assigned to each text prediction source. The ranking or priority values may be indicative of which text prediction source is to be employed, the order with which a text prediction source is applied, a weighting to be assigned to the output of a text prediction source, or the like. 
     According to at least one example embodiment, the adaptive text prediction module  120  is coupled to a database  140 . The database  140  may store configuration parameters  104  associated with each configuration, implementations of different configurations, pointers or application programming interfaces (APIs) for implementations of the different configurations or the like. The database  140  may alternatively, or in addition, store APIs or implementations of the multiple text prediction sources. As such, a particular configuration may be implemented on the fly by the adaptive text prediction module  120  using the stored APIs or implementations of the multiple text prediction sources. 
     Upon determining a configuration of the multiple text prediction sources to be employed, the adaptation module  124  may inform or instruct an execution module  128  about the determined configuration. The execution module  128  is configured to receive the text data  102 , the text transcript  103 , or both. The execution module  128  then applies the determined configuration of the plurality of text prediction sources to one or more of the received a-priori text interpretations, e.g.,  102  and  103 . Instructions from the adaptation module may further include an indication of the a-priori text interpretation(s) to be used. Alternatively, such indication may be inherent in the configuration determined or selected by the adaptation module. The execution module may further be configured to retrieve the pointer(s), API(s), or implementation(s) of the selected configuration or of the respective multiple text predictions from the database  140 . 
     By executing the selected configuration of the multiple text prediction sources, the execution module  128  generates an output text prediction  109 . The output text prediction  109  may be used as a final text transcript of the voice data  101 . Alternatively, the output text prediction  109  may be presented to a second agent, through an agent device  190 , to facilitate the generation of the final transcript of the voice data  101  by the second agent. In other words, the second agent may be provided with the voice data audio and the output text prediction  109 . According to an example scenario, the output text prediction  109  may be used, for example, as part of an interactive tool which provides or displays prediction(s) of a next word as the second agent types the text transcript of the voice data. According to another scenario, the output text prediction  109  may be presented to the second agent as a document to be reviewed and edited by the second agent while listening to the voice data audio. 
       FIG. 2A  is a block diagram illustrating an example configuration  200   a  of the multiple text prediction sources. The example configuration  200   a  of the multiple text prediction sources describes a sequential arrangement of two text prediction sources, a language model (LM) module  210   a  followed by a lattice decoder module  220   a . In other words, the a-priori text interpretation  202   a , e.g., including text data  102 , text transcript  103 , or both, is first fed to the LM module  210   a , which, in turn, generates a first text prediction  207   a . According to another scenario, no input is fed to the LM module and the LM module, in such case, provides the most likely prediction  207   a  based only on its internal statistical model. For example, for en-US voicemail messages, the LM prediction with no input is likely to be something like “Hi Mike. It&#39;s John. Can you call me back?” Where Mike is the most likely recipient name, and John is the most likely caller name. The first text prediction  207   a  is then fed to the lattice decoder module  220   a . The lattice decoder module  220   a  also receives a lattice  205   a  from the speech recognition module  110 . The lattice is typically a finite state machine representing multiple potential interpretations of the voice data generated by the speech recognition module  110 . The lattice decoder module  220   a  is configured to parse the lattice  205   a  and generate a second text prediction  208   a . The second text prediction  208   a  corresponds to the output text prediction  109 . The second text prediction  208   a  is generated based on the parsed information in the lattice  205  and the output of the LM module  207   a . The LM module  210   a  may generate more than one text prediction  207   a , each with an assigned respective probability or likelihood measure/score. The more than one text predictions  207   a  generated by the LM module are then used by the lattice decoder module  220   a  to generate the second text prediction  208   a . The lattice decoder module  220   a  may employ some weighting of the text prediction(s) provided by the LM module  210   a  and the interpretations embedded in the lattice to generate the second text prediction  208   a.    
     In a sequential configuration, such as  200   a , of the multiple text prediction sources, the order of the different text prediction sources is important. For example, in the configuration  200   a  the LM module  220   a  is applied first, and the lattice decoder module  210   a , is applied second. In an alternative sequential configuration, the lattice decoder module  210   a  is applied first followed by the LM module  220   a . The order of the multiple text prediction sources, or the corresponding configuration, is determined by the adaptation module  124  based on features  105  such as the complexity of the lattice, the signal-to-noise ratio of the voice data  101 , the text transcript  103  provided by the first agent, or a combination thereof. With regard to the complexity of the lattice, the more uncertainty is associated with the output of the speech recognition module  110 , the more complex the lattice is, and the simpler the lattice is, the more reliable the output of the speech recognition module is. In other words, the complexity of the lattice may be viewed as a measure of the reliability of the lattice. 
     According to at least one example embodiment, if the lattice is determined, e.g., based on a complexity measure, to be simple, the lattice decoder module  220   a  is applied in the beginning of a sequential configuration. Considering a configuration having a LM module  210   a  and a lattice decoder module  220   a , for example, the order of the LM module  210   a  and the lattice decoder module  220   a  would be reversed compared to the configuration in  FIG. 2A  when the lattice is found to be relatively simple. According to one scenario, a complexity measure of the lattice is the difference between the likelihood or probability scores associated with the best interpretation, e.g., path with highest probability, and the second best interpretation, e.g., path with second highest probability, in the lattice. The larger the difference between such scores, the simpler the lattice is and vise versa. According to another scenario, an alternative complexity measure of the lattice may be the total number of paths in the lattice  205   a . A person of ordinary skill in the relative art should appreciate that other complexity measures may be used. 
     The adaptation module  124  may also use the signal-to-noise ratio of the voice data to determine the configuration of the text prediction sources, or the order of the text prediction sources within the configuration. A high signal-to-noise ratio of the voice data may lead to reliable performance by the speech recognition module  110  and thus may be indicative of a reliable lattice  205   a . As such, in the case of a high signal-to-noise ratio, the lattice decoder module  220   a  precedes the LM module in the determined configuration. If the signal-to-noise is low, the LM module  210   a  precedes the lattice decoder module  220   a  in the configuration determined by the adaptation module  124 . In addition, a high signal-to-noise ratio may also be indicative of reliability of the text transcript  103  generated by the first agent, if available. 
       FIG. 2B  is a block diagram illustrating an example configuration  200   b  of the multiple text prediction sources. In the configuration  200   b , the LM module  210   b  and the lattice decoder module  220   b  are implemented in parallel. The a-priori text prediction  202   b  and the text transcript from a first agent are optional and the LM module  210   b  may still provide a first text prediction  207   b  with no a-priori text prediction  202   b  being fed to the LM module  210   b . The text predictions  207   b  and  208   b , e.g., outputs of the LM module  210   b  and the lattice decoder module  220   b , respectively, are fed to a weighting module  240   b . The weighting module  240   b  applies weightings, or assigns priority scores, to the text predictions  207   b  and  208   b  in order to generate output text prediction  209   b . The weighting module  240   b  may further use the text transcript  103  generated by the first agent, if it&#39;s available, in generating the output text prediction  209   b.    
     The weighting module  240   b  uses features  105  provided by the feature extraction module  130  to determine the weights to be applied to each of the text predictions, e.g.,  207   b  and  208   b , or transcripts, e.g.,  103 , provided to the weighting module  240   b . Such features  105  include, for example, the signal-to-noise ratio of the voice data  101 , the complexity of the lattice  205 , a measure of the accuracy or quality of the text transcript  103 , or the like. The weighting module  240   b  may further use other criteria in applying weighting, or assigning scores, to each of the text predictions, e.g.,  207   b  and  208   b , or transcripts, e.g.,  103 , provided to the weighting module  240   b . For example, each of the text prediction sources, e.g.,  210   b  and  220   b , may generate more than one text prediction, e.g.,  207   b  and  208   b . In such case, the weighting module  240   b  may assign high scores, or apply large weighting, to a text prediction, or a portion of a text prediction, that is the output of more than one text prediction source. For example, a text prediction, or a sentence therein, that appears in the output of the LM module  210   b  and the lattice decoder module  220   b  is assigned a relatively higher score than another text prediction, or a sentence therein, that appears in the output of a single text prediction source among  210   b  and  220   b . The weighting module  240   b  may process text predictions or portions of a text prediction when applying weightings or assigning priority scores. 
     In the case of a high signal-to-noise ratio of the voice data  101 , text prediction(s)  208   b  generated by the lattice decoder module  220   b  is/are assigned higher priority scores, or larger weightings, than text prediction(s)  207   b  generated by the LM module  210   b . In the case of a low signal-to-noise ratio of the voice data  101 , however, text prediction(s)  207   b  generated by the LM module  210   b  is/are assigned higher priority scores, or larger weightings, than text prediction(s)  208   b  generated by the lattice decoder module  220   b . Text prediction(s)  208   b  generated by the lattice decoder module  220   b  may also be assigned relatively high score(s), or relatively large weighting(s), if the lattice  205   b  has low complexity. However, if the lattice is determined to be complex, based on a complexity measure, the text prediction(s)  208   b  generated by the lattice decoder module  220   b  is assigned relatively low score(s), or relatively small weighting(s). The transcript  103 , if provided to the weighting module  240   b , may also be assigned a score or weighting. The weightings or scores are provided by the adaptation module  124  to the weighting module  240   b.    
       FIGS. 3A and 3B  show tables illustrating example weighting coefficients assigned to different prediction sources under different signal-to-noise ratios. The table in  FIG. 3A  corresponds to weightings or scores used in the case of low signal-to-noise ratio of the voice data  101 . The first column of the table in  FIG. 3A  shows scores or weightings for the outputs of LM module  210   b  and the lattice decoder module only as no transcript from the first agent is provided. The text prediction(s)  207   b  from the LM module  210   b  is/are assigned a score or weighting of 1, while the text prediction(s)  208   b  from the lattice decoder module  220   b  is/are assigned a score of 0.75. Such scores may be used, for example, to rank the different inputs to the weighting module  240   b  and provide the highest ranked one as the output text prediction  209   b . In the case where the LM module  210   b , the lattice decoder module  220   b , or both provide multiple text predictions with associated probability or likelihood values, the scores or weightings may be used in combination with such probability or likelihood values to determine the output text prediction  209   b . For example, the probability or likelihood values may be multiplied with the corresponding weightings or scores to generate new scores to be used in determining the output text prediction  209   b . Alternatively, a different rule or formula may be used to determine the output text prediction  209   b . The second column of the table in  FIG. 3A  shows the scores 0.7, 0.9 and 1, assigned to the text prediction(s)  207   b , the text prediction(s)  208   b , and the transcript  103 , respectively. The score values represent relative weights associated with different predictions. 
     The table in  FIG. 3B  illustrates examples scores or weightings used in the case of high signal-to-noise ratio of the voice data  101 . The first column corresponds to the case where no text transcript  103  is provided by the first agent and the second column corresponds to the case where a text  103  transcript is provided by the first agent. Relatively high scores are assigned, in this case, to outputs from both the LM module  210   b  and the lattice decoder module  220   b.    
       FIG. 4  is a flow chart illustrating a method  400  of text prediction according to at least one example embodiment. At block  410 , a configuration of a plurality of prediction sources, used for text prediction, is determined based at least in part on one or more features  105  related to voice data  101  and/or one or more a-priori text interpretations. In determining the configuration, the features  105  may be analyzed, and a decision is then made based on the analysis of the features  105 . One or more scores, or weightings, may be assigned to outputs of different text prediction sources based on the features  105  or the analysis thereof. The text prediction sources may be ranked based on the features  105 . The method may further include extracting the features. At block  420 , an output text prediction, e.g.,  109 ,  208   a  or  209   b , of the voice data  101  is generated using the plurality of prediction sources according to the determined configuration. 
     It should be understood that the example embodiments described above may be implemented in many different ways. In some instances, the various methods and machines described herein may each be implemented by a physical, virtual or hybrid general purpose or application specific computer having a central processor, memory, disk or other mass storage, communication interface(s), input/output (I/O) device(s), and other peripherals. The general purpose or application specific computer is transformed into the machines that execute the methods described above, for example, by loading software instructions into a data processor, and then causing execution of the instructions to carry out the functions described, herein. 
     As is known in the art, such a computer may contain a system bus, where a bus is a set of hardware lines used for data transfer among the components of a computer or processing system. The bus or busses are essentially shared conduit(s) that connect different elements of the computer system, e.g., processor, disk storage, memory, input/output ports, network ports, etc., that enables the transfer of information between the elements. One or more central processor units are attached to the system bus and provide for the execution of computer instructions. Also attached to the system bus are typically I/O device interfaces for connecting various input and output devices, e.g., keyboard, mouse, displays, printers, speakers, etc., to the computer. Network interface(s) allow the computer to connect to various other devices attached to a network. Memory provides volatile storage for computer software instructions and data used to implement an embodiment. Disk or other mass storage provides non-volatile storage for computer software instructions and data used to implement, for example, the various procedures described herein. 
     Embodiments may therefore typically be implemented in hardware, firmware, software, or any combination thereof. 
     In certain embodiments, the procedures, devices, and processes described herein constitute a computer program product, including a computer readable medium, e.g., a removable storage medium such as one or more DVD-ROM&#39;s, CD-ROM&#39;s, diskettes, tapes, etc., that provides at least a portion of the software instructions for the system. Such a computer program product can be installed by any suitable software installation procedure, as is well known in the art. In another embodiment, at least a portion of the software instructions may also be downloaded over a cable, communication and/or wireless connection. 
     Embodiments may also be implemented as instructions stored on a non-transitory machine-readable medium, which may be read and executed by one or more processors. A non-transient machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine, e.g., a computing device. For example, a non-transient machine-readable medium may include read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; and others. 
     Further, firmware, software, routines, or instructions may be described herein as performing certain actions and/or functions of the data processors. However, it should be appreciated that such descriptions contained herein are merely for convenience and that such actions in fact result from computing devices, processors, controllers, or other devices executing the firmware, software, routines, instructions, etc. 
     It also should be understood that the flow diagrams, block diagrams, and network diagrams may include more or fewer elements, be arranged differently, or be represented differently. But it further should be understood that certain implementations may dictate the block and network diagrams and the number of block and network diagrams illustrating the execution of the embodiments be implemented in a particular way. 
     Accordingly, further embodiments may also be implemented in a variety of computer architectures, physical, virtual, cloud computers, and/or some combination thereof, and, thus, the data processors described herein are intended for purposes of illustration only and not as a limitation of the embodiments. 
     While this invention has been particularly shown and described with references to example embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.