System with composite statistical and rules-based grammar model for speech recognition and natural language understanding

Abstract

The present invention thus uses a composite statistical model and rules-based grammar language model to perform both the speech recognition task and the natural language understanding task.

What is claimed is:  
     
         1 . A speech processing system, comprising: 
 an acoustic model;    a composite language model including a rules-based model portion and a statistical model portion; and    a decoder coupled to the acoustic model and the composite language model and configured to map portions of a natural language speech input to pre-terminals and slots, derived from a schema, based on the acoustic model and the composite language model.    
     
     
         2 . The speech processing system of  claim 1  wherein the decoder is configured to map portions of the natural language speech input to the slots based on the rules-based model portion of the composite language model.  
     
     
         3 . The speech processing system of  claim 1  wherein the decoder is configured to map portions of the natural language speech input to the pre-terminals based on the statistical model portion of the composite language model.  
     
     
         4 . The speech processing system of  claim 1  wherein the statistical model portion of the composite language model comprises: 
 a plurality of statistical n-gram models, one statistical n-gram model corresponding to each pre-terminal.  
 
     
     
         5 . The speech processing system of  claim 4  wherein the composite language model supports a vocabulary of words and wherein the statistical n-gram models are trained based on training data, and wherein words in the vocabulary that are not used to train a specific statistical n-gram model comprise unseen words for the specific statistical n-gram model.  
     
     
         6 . The speech processing system of  claim 5  wherein the statistical model portion of the composite language model further comprises: 
 a backoff model portion which, when accessed, is configured to assign a backoff score to a word in the vocabulary.  
 
     
     
         7 . The speech processing system of  claim 6  wherein each statistical n-gram model includes a reference to the backoff model portion for all unseen words.  
     
     
         8 . The speech processing system of  claim 7  wherein the backoff model portion comprises: 
 a uniform distribution n-gram that assigns a uniform score to every word in the vocabulary.  
 
     
     
         9 . The speech processing system of  claim 1  wherein the rules-based model portion comprises: 
 a context free grammar (CFG).  
 
     
     
         10 . A method of assigning probabilities to word hypotheses during speech processing, comprising: 
 receiving a word hypothesis;    accessing a composite language model having a plurality of statistical models and a plurality of rules-based models;    assigning an n-gram probability, with an n-gram model, to the word hypothesis if the word hypothesis corresponds to a word seen during training of the n-gram model; and    referring to a separate backoff model for the word hypothesis if the word hypothesis corresponds to a word unseen during training of the n-gram model; and    assigning a backoff probability to each word hypothesis, that corresponds to an unseen word, with the backoff model.    
     
     
         11 . The method of  claim 10  and further comprising: 
 mapping the word hypotheses to slots derived from an input schema based on the rules-based models in the composite language model.  
 
     
     
         12 . The method of  claim 11  and further comprising: 
 mapping the word hypotheses to pre-terminals derived from the input schema based on probabilities assigned by the n-gram models and the backoff model in the composite language model.  
 
     
     
         13 . The method of  claim 12  wherein referring to a separate backoff model comprises: 
 referring to a uniform distribution n-gram.  
 
     
     
         14 . The method of  claim 13  wherein assigning a backoff probability comprises: 
 assigning a uniform distribution score to every word in the vocabulary.  
 
     
     
         15 . A composite language model for use in a speech recognition system, comprising: 
 a rules-based model portion accessed to map portions of an input speech signal to slots derived from a schema; and    a statistical model portion accessed to map portions of the input speech signal to pre-terminals derived from the schema.    
     
     
         16 . The composite language model of  claim 15  wherein the statistical model portion comprises: 
 a plurality of statistical n-gram models, one statistical n-gram model corresponding to each pre-terminal.  
 
     
     
         17 . The composite language model of  claim 15  wherein the rules-based model portion comprises: 
 a context free grammar (CFG).  
 
     
     
         18 . The composite language model of  claim 16  wherein the composite language model supports a vocabulary of words and wherein the statistical n-gram models are trained based on training data, and wherein words in the vocabulary that are not used to train a specific statistical n-gram model comprise unseen words for the specific statistical n-gram model.  
     
     
         19 . The composite language model of  claim 18  wherein the statistical model portion of the composite language model further comprises: 
 a backoff model portion which, when accessed, is configured to assign a backoff score to a word in the vocabulary.  
 
     
     
         20 . The composite language model of  claim 19  wherein each statistical n-gram model includes a reference to the backoff model portion for all unseen words.  
     
     
         21 . The composite language model of  claim 20  wherein the backoff model portion comprises: 
 a uniform distribution n-gram that assigns a uniform score to every word in the vocabulary.