Patent Document

TECHNICAL BACKGROUND  
         [0001]    1. Technical Field  
           [0002]    The system and method described herein relate to conversational spoken dialogue systems.  
           [0003]    2. Discussion of the Related Art  
           [0004]    The main components of a conventional spoken dialogue system are a speech recognizer and an understanding module. During a single operation of the system (i.e., one “recognition pass”), the speech recognizer generates a hypothesis of the words from a spoken utterance, and the understanding module interprets the intent of the spoken query; performing an associated action (such as data retrieval) based upon this interpretation. Mis-recognition by the speech recognizer can therefore result in the implementation of an undesirable or inappropriate action by the understanding module. This mis-recognition can be frustrating to the user, and is a limitation of many conventional spoken dialogue systems.  
           [0005]    Speech recognizers typically use a language model to identify the regularities of the language in the specific domain (e.g., airline reservations, medical information, legal issues). There are two basic models for such speech recognition: a grammar-based model and a statistical language model (SLM). In a grammar-based model, specific rules are implemented such that a speech recognizer may recognize phraseology common to the domain. While this generally yields good recognition accuracy, such a model is not robust with regard to the disfluencies associated with speech. An utterance may become unrecognizable to the system when phrased in a manner for which the system is not pre-programmed. Moreover, the inclusion of a greater number of grammar rules quickly reduces the speed of the system, often rendering the system impractical to use. SLMs are typically derived from a large database and are implemented in a fast search algorithm. Yet, while SLMs may be more adept at handling speech disfluencies, they are often less accurate than grammar-based models. Thus, one is left to the trade-off between speed and accuracy.  
           [0006]    To improve accuracy without significantly sacrificing speed, some spoken dialog systems utilize an acoustic confidence scoring mechanism that indicates a confidence level in the hypothesis generated by the speech recognizer. Based on the confidence level, an error correction mechanism may be employed (i.e., if the confidence level is sufficiently low, the error correction mechanism is triggered). One such mechanism may request the user to repeat the query, rather than risking the performance of an undesirable or inappropriate action by the system.  
           [0007]    Most systems that employ a confidence scoring mechanism do not include any additional mechanism that might improve the confidence score. Instead, an utterance is processed through the system once; a single confidence score is generated, and no effort is made to reevaluate the utterance or hypothesis to improve the score. Furthermore, most systems do not return intelligent feedback to the user, prompting him/her as to which part of the spoken request was unclear.  
           [0008]    In one system, a series of confidence scores are derived from the same recognition pass. These scores are generated to identify unreliable words in an utterance; thereby allowing the system to query the user for specific information. However, this system does not implement any mechanism that operates to improve any of the confidence scores if they are low. T. J. Hazen et al., “Recognition Confidence Scoring for Use in Speech Understanding Systems,”  Proc. ISCA ASR 2000  Tutorial and Research Workshop,  Paris (2000).  
           [0009]    Accordingly, there is a need for a system and method for speech recognition in a spoken dialogue system that obviates, for practical purposes, the above-mentioned limitations. Such a system and method may offer high-quality speech recognition by reexamining utterances that return a low confidence score when processed through a speech recognizer. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0010]    [0010]FIG. 1 depicts a schematic representation of a multi-pass spoken dialogue system in accordance with an embodiment of the present invention;  
         [0011]    [0011]FIG. 2 depicts a schematic representation of a multi-pass spoken dialogue system in accordance with another embodiment of the present invention; and  
         [0012]    [0012]FIG. 3 depicts a flow chart representation of a multi-pass speech recognition method in accordance with an embodiment of the present invention. 
     
    
     DETAILED DESCRIPTION  
       [0013]    Embodiments of the present invention provide a system and method for speech recognition in a spoken dialogue system. In various embodiments, the system and method implement a multi-pass approach to speech recognition that may improve recognition accuracy. These embodiments may implement an SLM of speech recognition in one recognition pass, and a grammar-based model of speech recognition in another recognition pass. This scheme may improve recognition accuracy of low confidence recognition results from a first recognition pass, thus providing an automatic recovery mechanism for the speech recognizer. As used herein, “language models” include domain-specific language models, which are language models that include information particular to a defined set of data.  
         [0014]    As depicted in FIG. 1, an embodiment of a multi-pass speech recognition system  100  of the present invention may include a first speech recognizer  102  and a second speech recognizer  109 . The first speech recognizer  102  may implement a first recognition pass in response to a spoken utterance  101  input to the system  100  by a user. The first recognition pass may utilize a language model  103 , such as an SLM or a grammar-based model, though it will be recognized by one in the art that other suitable language models may be implemented, and are contemplated as being alternate embodiments of the present invention.  
         [0015]    The second speech recognizer  109  may similarly implement a second recognition pass in response to a spoken utterance  101  input to the system  100  by a user. The second speech recognizer  109  may utilize another language model  108  to implement a second recognition pass. The language model  108  utilized by the second speech recognizer  109  may be more constrained than the language model  103  utilized in the first speech recognizer  102 . By way of example, in FIG. 1, the first speech recognizer  102  implements an SLM  103  while the second speech recognizer  109  implements a grammar-based model  108  that may be based on application-specific pre-programmed series of grammar-based rules, and may be further dynamically constrained by the keyword spotted by a word spotting speech recognizer  105 . Notably, where computing system capacity allows, the first and second recognition pass may be processed in parallel, potentially reducing the overall operation time of the multi-pass system  100 .  
         [0016]    The multi-pass system  100  may further include a first confidence estimator  104  and a second confidence estimator  110 ; the confidence estimators  104  and  110  generating a first confidence score C F  and a second confidence score C S , respectively, based upon the perceived accuracy of the first and the second recognition pass, respectively. The first and second confidence estimators  104  and  110  may each further include an associated threshold level, T F  and T S , respectively. The confidence score generated by a confidence estimator may be compared with its associated threshold level to determine an appropriate course of action in implementing the logic of the multi-pass recognition system  100 . For instance, a confidence score at or above the threshold level may implicate speech understanding  113 , while a confidence score below the threshold level may indicate that further speech analysis or a user prompt is desirable.  
         [0017]    The multi-pass system  100  may further include a word-spotting speech recognizer  105  between the first and second speech recognizers,  102  and  109 . By identifying discrete words or phrases (“keywords”  106 ) in an utterance, a word-spotting speech recognizer  105  may dynamically reduce the number of rules that must be searched in a second speech recognizer  109  implementing a grammar-based model  108 , potentially improving system speed. For example, by providing a keyword hypothesis to a grammar-based second speech recognizer  109 , the second speech recognizer  109  may focus the rules through which it searches to those terms or phrases that include the keyword. Furthermore, the word-spotting speech recognizer  105  may enable the system  100  to provide intelligent feedback to the user (e.g., prompting the user to repeat specific, unrecognizable portions  112  of the utterance  101 ).  
         [0018]    The word-spotting speech recognizer  105  may further include a confidence estimator  107  that generates a confidence score C W , similar to those described above with respect to the first and second speech recognizers,  102  and  109 . Based upon a comparison of the confidence score C W  with a threshold level T W , the system  100  may determine to proceed with multi-pass speech recognition by suggesting a keyword to the second speech recognizer  109  (i.e., confidence score C W  is at or above the threshold level T W ), or the system  100  may prompt the user to repeat the entire utterance  111  (i.e., confidence score C W  is below the threshold level T W ).  
         [0019]    As depicted in FIG. 2, another embodiment of a multi-pass speech recognition system  200  of the present invention may include a first speech recognizer  202  and a second speech recognizer  209 , similar to those described in the embodiment above (i.e., implementing a recognition pass in response to a spoken utterance  201  input to the system  200  by a user). The first and second recognition passes may each utilize language models  203  and  208 , respectively, such as an SLM or a grammar-based model. Also, where computing system capacity allows, the first and second recognition pass may be processed in parallel, potentially reducing the overall operation time of the multi-pass system  200 .  
         [0020]    The multi-pass system  200  may further include first and second confidence estimators  204  and  210 ; the confidence estimators  204  and  210  generating a first confidence score C F  and a second confidence score C S , respectively, based upon the perceived accuracy of the first and the second recognition pass, respectively. The first and second confidence estimators  204  and  210  may each further include an associated threshold level, T F  and T S , respectively. The confidence score generated by a confidence estimator may be compared with its associated threshold level to determine an appropriate course of action in implementing the logic of the multi-pass recognition system  200 . For instance, a confidence score at or above the threshold level may implicate speech understanding  213 , while a confidence score below the threshold level may indicate that further speech analysis (i.e., confidence score C F  is below the threshold level T F ) or a user prompt  212  to repeat the utterance (i.e., confidence score C S  is below the threshold level T S ) is desirable.  
         [0021]    In another embodiment of the present invention, as depicted in FIG. 3, a method of multi-pass speech recognition may begin with user input  301  of an utterance. The utterance may be processed  302  through a first recognition pass by a first speech recognizer, thereby generating  303  a first sentence hypothesis. A confidence score may be indicated  304  for the first sentence hypothesis, and may thereafter be compared  305  to a predetermined first threshold level. If the confidence score is at or above the first threshold level, the system may take  313  a desired action. If the score is below the first threshold level, then further analysis may ensue.  
         [0022]    Keywords in the utterance may be identified  306  by a word-spotting speech recognizer. A confidence score may be indicated  307  for the hypothesized keyword, and may thereafter be compared  308  to a predetermined threshold level. If the confidence score is at or above the word-spotting threshold level, the keyword may be suggested to a second speech recognizer; thereby reducing the rules through which the second speech recognizer must search, where the second speech recognizer implements a grammar-based model of speech recognition. If the score is below the word-spotting threshold level, however, then the system may prompt  314  the user to repeat the entire utterance.  
         [0023]    The utterance may be further processed  309  through a second recognition pass by a second speech recognizer, thereby generating  310  a second sentence hypothesis. A confidence score may be indicated  311  for the second sentence hypothesis, and may thereafter be compared  312  to a predetermined second threshold level. If the confidence score is at or above the second threshold level, the system may take  313  a desired action. If the score is below the second threshold level, then the system may prompt  315  the user to repeat the entire utterance or only information related to a keyword (i.e., if a word-spotting speech recognizer is included).  
         [0024]    While the description above refers to particular embodiments of the present invention, it will be understood that many modifications may be made without departing from the spirit thereof The accompanying claims are intended to cover such modifications as would fall within the true scope and spirit of other embodiments of the present invention. The presently disclosed embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims, rather than the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

Technology Category: 3