Abstract:
A system and method is disclosed for Report Confidence Modeling (RCM) including automatic adaptive classification of ASR output documents to determine the most efficient document edit workflow to convert dictation into finished output. The RCM according to the present invention may include a mechanism to predict recognition accuracy of a document generated by an ASR engine. Predicted accuracy of the document allows an ASR application to sort recognized documents based on their estimated accuracy or quality and route them appropriately for further processing, editing and/or formatting.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of priority from U.S. Provisional Application Ser. No. 60/592,123, filed Jul. 30, 2004 entitled “A System and Method for Report Level Confidence”. 
    
    
     BACKGROUND OF THE INVENTION 
     The present invention relates generally to a system and method for classifying the confidence or quality of an automatically transcribed report or document. 
     Today&#39;s speech recognition technology enables a computer to transcribe spoken words into computer recognized text equivalents. Automatic Speech Recognition (ASR) is the process of converting an acoustic signal, captured by a transducive element, such as a microphone or a telephone, to a set of text words in a document. These words can be used for numerous applications including data entry and word processing. The development of speech recognition technology is primarily focused on accurate speech recognition. 
     The accuracy of a speech recognition system or a recognizer depends on many different variables including accents, regional language differences, subject matter and speech patterns. Because of this variability in accuracy, automatically transcribed documents typically require editing to correct errors made by the recognizer during transcription. In some cases, the error rate of a recognizer may be too high and the amount of editing required for a given document with a low recognition accuracy may require more effort, time, and cost to edit than if the given document had been transcribed by a human transcriptionist in the first place. This dilemma often results in low consumer confidence in speech recognition systems or even abandonment of automatic speech recognition systems in environments where the recognizer accuracy is low. 
     As a result, Report Confidence Modeling (RCM) systems have been devised to rate and score a particular ASR system. A typical RCM system includes a mechanism to predict recognition accuracy by an ASR system. Predicted accuracy allows an ASR system to sort recognized documents based on their estimated accuracy (quality) and route them appropriately for further processing (for example, editing and formatting). 
     The idea of sorting recognized documents by predicted recognition accuracy comes from the assumption that editing recognized documents (correcting misrecognitions) provides productivity gains compared to typing if recognition quality is good (higher than a certain threshold). If recognition accuracy is not good enough, it is more efficient to type the document rather than correct misrecognitions. Accuracy of text generated by ASR can be predicted based on several factors, including, but not limited to, (a) the confidence values of recognized words; (b) the lexical classes of certain recognized words; (c) temporal characteristics of the recognized report; and (d) speaker&#39;s historical behavior. RCM models may be static, factory models developed without reference to site-specific or user-specific data, or adapted models developed by collecting site-specific and user-specific data. 
     “Good” (i.e., those with high recognition accuracy) documents could be routed to transcriptionists or self-editing doctors for editing (error correction, editing, and formatting) while “bad” (i.e., those with low recognition accuracy) documents could be routed for further ASR processing or for being typed from scratch by a transcriptionist. 
     There has been significant research on, and development of, confidence rating systems and measures of ASR systems. Some traditional confidence rating systems are based on the probability of the acoustic observation given the speech segment normalized by the general probability of the acoustic observation. There have also been attempts to develop techniques for word confidence estimation that are independent of the architecture and operation of the word recognizer. Other confidence measurements systems use content level and semantic attributes, using the 10 best outputs of a speech recognizer and parsing the output with phrase level grammar. Still others use out-of-vocabulary words and errors due to additive noise to produce an acoustic confidence measure. 
     A drawback of each of the above mentioned systems is that they focus on the confidence of ASR system at the word level. This principle is known as dialogue management. Use of dialogue management is helpful to determine whether a particular statement has been reliably recognized and converted to text. This confidence rating can be combined with other tools to improve automatic transcription accuracy, such as a parser, to mitigate the dilemma of excessive editing of automatically transcribed documents. However, it is important to note again that these confidence measurements and tools are focused on a word level and are not combined to produce a document level confidence measurement. 
     Additional attempts to control the amount of editing include identifying certain speakers and accents that are poorly recognized by a recognizer or speech recognition engine. Those speakers can be identified in advance and dictations by those speakers may be routed directly to a human transcriptionist rather than a speech recognition system. 
     Despite this, traditional speech recognition systems suffer from an all or nothing condition. In other words, traditional systems are incapable of determining, in advance of editing an automatically transcribed document, the most efficient and cost effective workflow. This latter principle is known as document management. As stated above, traditional confidence measurement systems are limited to dialogue management and cannot determine whether it is more effective to employ a recognizer and subsequently edit the automatically transcribed document or to abandon the recognizer and the automatically transcribed document entirely for a traditional, strictly human transcription approach. 
     Therefore, there exists a need for a system and method of determining a document level confidence measurement for an entire automatically transcribed document. The document level confidence measurement may include not just quantifying the quality of the automatic transcription, but also quantifying additional factors affecting how easily the document may be edited or transcribed. 
     There also exists a need for a system and method for optimizing the workflow of transcribing dictations based the document level confidence measurement. 
     What is also needed is a system and method for, based on the document level confidence, determining the more efficient of two options: editing an automatically transcribed document or abandoning the automatically transcribed document for the traditional human transcription. 
     What is further needed is a speech application to implement a strategy of combining self editing and transcriptionists in a cost-effective manner. 
     SUMMARY OF THE INVENTION 
     An embodiment of the present invention includes a system and method for Report Confidence Modeling (RCM) including automatic adaptive classification of ASR output documents to determine the most efficient document edit workflow to convert dictation into finished output. RCM according to the present invention may include a mechanism to predict recognition accuracy of a document generated by an ASR engine. Predicted quality of the document allows an ASR application to sort recognized documents based on their estimated accuracy or quality and route them appropriately for further processing, editing and/or formatting. 
     Briefly, in transcriptionist environments it is important to have a way to select only high recognition accuracy documents to be edited as draft documents. The systems and methods described herein can be used to maximize editable reports by filtering on a per-document, rather than solely per-speaker basis. Poor quality documents can be filtered from generally good speakers and high-quality documents can be preserved from generally poor speakers. User may be presented with an indication of predicted quality along with the job output while having the option to choose his/her preferred editing method, Additionally, low-confidence jobs can be automatically filtered out of the transcription stream and a history of rate of high predicted accuracy jobs per speaker can be used to determine which jobs are sent for recognition, thereby optimizing server-use. 
     A method of evaluating speech recognition according to one aspect of the present invention methods may be based on the comparison between a literal transcript of what the speaker spoke and the output of a recognition engine or by analyzing signal quality. An advantage of this type of method as compared to traditional truth based scoring is that humane effort as part of workflow can be leveraged to customize based on perceived productivity, rather than only accuracy. As compared to historical scoring based on previously edited documents the present invention does not require waiting until documents are generated to implement and a user may begin scoring documents with factory model immediately. As compared to per-document scoring (reliability/efficiency), the per-word confidence can be confusing and may not focus upon information the user needs to predict his/her productivity, i.e., the overall quality of the document. 
     A final advantage of the of per-document, pre-edited score is that individual poor scoring documents may be filtered and quality is known in advance of editing for a particular document, rather than relying on historical scores. 
     As such, in a first embodiment, the present invention includes a method for optimizing document management using report level confidence in a speech recognition system by first receiving a dictation having an automatic speech recognizer output and an audio file. A confidence score is predicted using a predetermined method and the automatic speech recognizer output is classified using the predicted confidence score and predetermined filter settings. The dictation is then routed based upon the classified automatic speech recognizer output to a first editing location or a second editing location and converted into a finished product. The routing of the dictation is determined based upon the previously determined classification of the automatic speech recognizer output. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       While the specification concludes with claims particularly pointing out and distinctly claiming the present invention, it is believed the same will be better understood from the following description taken in conjunction with the accompanying drawings, which illustrate, in a non-limiting fashion, the best mode presently contemplated for carrying out the present invention, and in which like reference numerals designate like parts throughout the Figures, wherein: 
         FIG. 1  shows a high level flow diagram of a system and method according to one embodiment of the present invention; 
         FIG. 2  shows a flow diagram of user selected settings according to one embodiment of the present invention; and 
         FIG. 3  shows a flow diagram of a report confidence modeling adaptation module according to one embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The present disclosure will now be described more fully with reference to the figures in which embodiments of the present invention are shown. The subject matter of this disclosure may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. 
     Initially a recognized file will provide an indication of high or low projected recognition quality which indication is generated using a model and multiple features of recognition output including predicted accuracy. Certain models may be tuned to adjust threshold for high and low confidence by changing both threshold and the scoring strictness. Users may adjust the confidence flag for the tolerances of their transcription staff. If these parameters are not set, the default threshold and strictness will be used. A set of default Report Confidence Models may be provided that will generate useable predicted accuracy scores. After enough files have been collected user-specific and user/transcriptionist-specific models can be adapted. These adapted models may use comparisons of edited documents to recognized text, transcriptionist feedback and other available measures of historical speaker/transcriptionist behavior. 
     In one embodiment, a confidence measurement is generated during each recognition job using the default Report Confidence Model. An indication of predicted accuracy is inserted into the recognition output. A configurable setting allows the user to either automatically reject low-confidence jobs or preview all jobs. For jobs of both types, the transcriptionist has the option of editing or transcribing a report. It is assumed that most low-confidence jobs will be transcribed and most high-confidence jobs will be edited. Finished reports, regardless of transcription method, are returned to the server for storage and further routing. 
     Referring now to  FIG. 1 , a flow diagram representing the system and method of automatically and adaptively classifying automatic speech recognition (ASR) output documents to determine the most efficient transcription workflow path to convert dictation into finished textual output. ASR software is run by a computer having a central processing unit (CPU). The CPU executes a computer code operatively associated with the CPU, which includes various sets of instructions used by the CPU to run the ASR software. 
     The flow diagram may begin with dictated report  15  being fed into the ASR or recognizer  25 . Dictated report may consist of many different types of speech dictation across a variety of domains including medical reports, legal documents and police records. It is also contemplated that the delivery of dictated report may come from a multitude of different sources, such as computer systems, telephone systems, microphones, etc. The flow diagram as shown in  FIG. 1  may be combined with a central dictation system serving a hospital or other large organization with many different speakers or may also be combined with smaller departments or users which may input a dictated report directly into the ASR without the assistance of a central dictation system. 
     Dictated report  15  may be recognized by ASR  25  to produce recognized text  20 . Recognized text  20  may be produced by any commercial recognizer or ASR. Recognized text  20  may then be received in step  35  where a confidence score may be inserted into recognized text  20 . Confidence score  35  may be generated using input from RCM module  30 , the user selected settings  40  and recognized text  20 . The method of generating confidence score  35  will be discussed in detail below. 
     Confidence score  35  may be evaluated to determine if confidence score  35  is high enough  10  or has met a predetermined threshold in step  55  if confidence score  35  is high enough, then recognized text  20  may be determined accurate enough such that editing  60  recognized text  20  is the most efficient transcription workflow path. Alternatively, if confidence score  35  is determined to be too low, then recognized text  20  may be discarded such that dictated report  15  may be transcribed by a traditional human transcriptionist. Therefore, transcribing dictated  15  report using a human transcriptionist may be the most efficient transcription workflow path when confidence score  35  is determined to be too low. 
     It is important to note that confidence  35  may be more of a measurement of utility than purely a measurement of recognition accuracy. Confidence score  35  may indicate whether or not it is more utilitarian or efficient to edit recognized text  20  or to abandon recognized text  20  and simply transcribe dictated report  15 . As such, confidence score  35  could simply be an objective measure of the accuracy of recognized text  20  in one embodiment of the invention. However, the confidence report  35  may also include a more expansive approach to determining the factors affecting the editing and transcribing dictated report  15 . 
     Confidence score  35  and recognized text  20  may also be delivered to the report confidence modeling adaptation (RCMA) module  50 . RCMA module  50  may generate feedback data delivered to RCM module  30  used to improve the modeling capabilities of RCM module  30  and generate a more accurate confidence score  35 . 
     Referring to  FIG. 2 , a flow diagram details the inputs into predicting confidence score  35 . It is important to note that ASR systems produce recognized text and produce confidence measures for words in dictated report  15 . Other confidence measurements may also be provided by the ASR upon converting dictated report  15  into recognized text  20 . The different confidence measures generated by the ASR are provided to predict confidence score  35  along with recognized text  20  in  FIG. 2 . 
     As shown in  FIG. 2 , the user selected settings  40  may include inputs from a threshold  5  measure  41  and a strictness measure  42 . Threshold  41  may be used to adjust how high confidence score  35  must be in order for editing  60  recognized text  20  to be the most efficient workflow path. Strictness measure  42  may be used to provide adjustment to a standard error. This strictness  42  may allow users to minimize false positives or maximize the number of high confidence scores  35 . 
     The user selected settings  40  in  FIG. 2  allows for two different options. The first option may be to set a threshold  41  based on recognition accuracy or confidence score  35 . Therefore, it may be assumed that the system will only edit recognized text  20  when confidence score  35  reflects a certain level of accuracy. For example, threshold  41  may require documents with at least 90% of the words correct. The second option may be to set strictness  42  such that there is some degree of confidence score  35  that is acceptable even if confidence score  35  falls below threshold  41 . It is contemplated that these settings for threshold  41  and strictness  42  may be set for the entire system shown in  FIG. 1  or may be set for individual speakers and/or transcriptionists. It is also contemplated that these settings may be set according to different modes, different work type, or perhaps having to do with certain types of errors. 
     Additionally, threshold  31  and strictness  42  may also consider other factors. The relative cost of different operations may be considered in setting the user selected settings  40 . Further, it may be possible to actually adjust the factors by understanding the essential risk and cost of doing certain types of operations as opposed to the other. For example, the cost of doing formatting type corrections rather than content corrections may be considered as a factor in establishing strictness  42 . Likewise, operations such as making substitutions, correcting spelling, inserting or deleting text, and other such operation may also be considered in selecting the user selected settings  40 . 
     Although the system can be applied to all speakers and transcriptionists, the system shown in  FIG. 1  may also be applied to specific speakers and/or transcriptionists. In this situation, the system may be able to anticipate looking at any given speaker and/or any given a transcriptionist and assign appropriate user selected settings  40  specific to the speaker and/or transcriptionist. The system may also be able to estimate for a site what the actual the costs are for running the system for some workload at give thresholds  41  and strictness  42 . Users of the system may then be able to optimize the system further by adjusting the user selected settings  40  to optimize for specific requirements such as time efficiency and/or cost effectiveness. As such, the system as shown in  FIG. 1  may provide users the ability to more accurately manage a dictation and transcription system than ever before. 
     It is also possible that the user might use the system as shown in  FIG. 1  for the ability to look at the percentage of jobs for a particular speaker. If the dictated reports  15  for a particular speaker are consistently getting high or low confidence scores  35 , the user of the system may chose to use this information to short circuit the process and strictly have the speaker&#39;s dictated reports follow the most efficient transcription workflow path without the calculation of a confidence score  35 . As such, the different management information available to the user selected settings  40  and the information that may be extracted from the system may provide a variety of management modes with a variety of efficiencies of the management of the workload. This level of management would not be possible without the user selected settings  40  and confidence scores  35 . 
     Referring to  FIG. 3 , a flow diagram details RCMA module  50  receiving input from the marshal text pairs  51 , the transcriptionist feedback  52  and the transcription characteristics  53 . RCMA module  50  may act to analyze the dictated reports  15  that pass through the system and either are edited or transcribed by a transcriptionist. By analyzing previous dictated reports that have been converted into a final text document by editing or transcription, RCMA module  50  may identify common or recurring error as well as collect information from the transcriptionists regarding the cost and efficiency of the editing path  60  or transcription path  65 . 
     Marshal text pairs  51  may be provided to RCMA module  50  to improve recognized and finished text pairs. These pairs are collected from correctly recognized text documents as a form of feedback and allow RCMA module  50  to identify and highlight pairs of words that may have a low confidence level individually from the ASR but have a high confidence level when found in pairs. These text pairs  51  may then be directed to RCM module  30  to be used in predicting confidence score  35  for recognized text  20 . 
     The marshal text pairs  51  may also be used for pseudo-truth-generated accuracy. This pseudo-truth-generated accuracy may be used to improve the confidence level of documents. Further, the number of required edits may also be collected and used to predict confidence by comparing the recognized text to known spellings or phrases. The fewer the differences the greater the confidence in the recognized text. Other information may also be collected and used to predict the confidence of the recognized document. All of these may be analyzed and then provided to RCM module  30  to be used in predicting confidence score  35  for recognized text  20 . 
     Transcriptionist feedback  52  may also be provided to RCMA module  50  to aid in determining which dictated reports  15  would follow the editing path  60  or the transcription path  65 . Data may be collected on finished reports that received a high confidence score  35  and where edited. Data may also be collected on finished reports that received a low confidence score  35  and were traditionally transcribed. The data collected could include the performance evaluations, an overall rating of the job, etc. Other information can also be collected about the individual transcriptionist such as skill level and training. This transcriptionist feedback  52  may then be analyzed and provided to RCM module  30  to be used in predicting confidence score  35  for recognized text  20 . 
     Transcription characteristics  53  may also be provided to RCMA module  50  to aid in determining which dictated reports would follow the editing path  60  or the transcription path  65 . Data may be collected on the number and speed of keystrokes during editing or transcribing. The length of time taken for editing and transcribing individual reports may also be collected. The relationship between the transcription characteristics of a given dictated report  15  and the average statistics for finished reports may be analyzed to provide additional data. This data as well may be analyzed and provided to RCM module  30  to be used in predicting confidence score  35  for recognized text  20 . 
     Although  FIG. 3  shows RCMA module  50  receiving data from the marshal text pairs  51 , the transcriptionist feedback  52 , and the transcription characteristics  53  and providing this data to RCM module  30  in order to better model and assist in predicting confidence score  35 , it should be noted that RCMA module  50  may receive and deliver information in any combination or even  30  not at all depending on the information available at any given time. It is also contemplated that RCMA module  50  in providing data to RCM module  30  may consider some additional factors. By incorporating historical data from editing, transcribing, transcriptionists, and speakers, the overall system shown in  FIG. 1  may adapt to different dictation environments and optimize the system for the most efficient and cost effective workflow paths. The system may also adapt to changes in the system such as adding or subtracting transcriptionists. 
     It is also contemplated that the relationship between specific transcriptionist and specific speakers may be analyzed to determine a confidence score  35 . As skills between transcriptionists vary, it may be possible to adjust confidence score  35  depending on the specific speaker providing dictated report  15  and the specific transcriptionist performing the editing under step  60  and the transcribing under step  65 . 
     Referring now to RCM module  30 , the particular techniques that are used to create confidence score  35  and the actual inputs to the RCM module  30  are actually quite a bit different than the typical kinds techniques and inputs used also for other confidence measures. This may be attributed to the fact that other confidence measures are focused on word based confidence measures. Word based confidence measures tended to use, for example, purely acoustic features rather than actually bringing in other external linguistic or structural features, semantics, syntatic or other document type features. In fact, much of the word level confidence measures are exclusively on the subject basis. 
     However, RCM module  30  actually incorporates essentially the broadest possible range  20  of features into confidence score  35  because RCM module  30  needs not only to understand core recognition but also all the structural configurational attributes of the document. These structural configurational attributes directly affect how efficiently and cost effectively recognized text  20  can be edited in step  60 . It is important to note that the broad range of features are considered in establishing confidence score  35  because confidence score  35  may be evaluating a document containing many words. 
     Therefore, in producing a confidence score  35 , RCM module  30  considers what kind of factors or features are relevant and how to actually combine those features into a confidence score  35 . RCMA module  50  discussed above may additionally aid the process by providing historical data and other information that may be used to indicate how confidence score  35  actually relates to the most efficient transcription workflow path. Machine learning algorithms and classification methods may be used to evaluate the factors and features received in RCM module  30  in order to produce a model for predicting confidence score  30 . Recognized text  20  may include components relating to individual word confidences and other information that is produced by the recognizer  25  along with recognized text  20  that may be interpreted and used by RCM module  30 . It must also contemplated that RCM module  30  may be an original model or may be a trained model after receiving input from RCMA module  50 . Machine learning algorithms may be used train RCMA module  50  and the RCM module  30 . 
     Referring again to the system shown in  FIG. 1 , thresholds, established in the user selected settings  40 , may be used as breakpoints and relate confidence score  35  to a decision on workflow efficiency. This decision may act as making cuts between the different groups: those recognized reports  20  that will be edited and those recognized reports  20  that will be discarded and completely transcribed by a human transcriptionist. 
     It will be apparent to one of skill in the art that described herein is a novel system and method for automatically generating report-level confidence. While the invention has been described with reference to specific preferred embodiments, it is not limited to these embodiments. The invention may be modified or varied in many ways and such modifications and variations as would be obvious to one of skill in the art are within the scope and spirit of the invention and are included within the scope of the following claims.