Discriminative training of hidden Markov models for continuous speech recognition

Abstract

Methods are given for improving discriminative training of hidden Markov models for continuous speech recognition. In one approach, discriminatively trained mixture models are interpolated with maximum likelihood trained mixture models. In another approach, segmentation and recognition results from one set of models are reused to discriminatively train a second set of models. For example, segmentation and recognition results from detailed match models are mapped and used to discriminatively train fast match models. In addition, gradients for the standard deviation of mixture components are clipped based on the statistics of the gradients. Pronunciation of words may also be used to determine the “incorrect” recognition hypothesis.

What is claimed is:  
     
         1 . A method of a continuous speech recognition system for discriminatively training hidden Markov models, the method comprising: 
 performing segmentation and recognition of speech training data using a first set of recognition models so as to form a first model reference state sequence, and a set of first model hypothesis state sequences;    mapping states in the first model reference state sequence to corresponding states in a second set of recognition models so as to form a second model reference state sequence;    mapping states in the set of first model hypothesis sequences to corresponding states in the second set of recognition models so as to form a set of second model hypothesis sequences; and    discriminatively training selected model states in the second set of recognition models using the mapped state sequences.    
     
     
         2 . A method according to  claim 1 , wherein the hypothesis state sequences are represented by a lattice structure.  
     
     
         3 . A method according to  claim 1 , wherein the first set of recognition models are detailed match models, and the second set of recognition models are fast match models.  
     
     
         4 . A method of a continuous speech recognition system for discriminatively training hidden Markov models, the method comprising: 
 for a mixture component of a hidden Markov model state, calculating a gradient adjustment of the standard deviation of the mixture component, and 
 i. if the calculated gradient adjustment is greater than a first threshold amount, performing an adjustment of the standard deviation of the mixture component using the first threshold, or  
 ii. if the calculated gradient adjustment is less than a second threshold amount, performing an adjustment of the standard deviation of the mixture component using the second threshold, or else  
 iii. performing an adjustment of the standard deviation of the mixture component using the calculated gradient adjustment.  
   
     
     
         5 . A method of a continuous speech recognition system for discriminatively training hidden Markov models, the method comprising: 
 determining correctness of a hypothesized word using pronunciation of the hypothesized word and a corresponding word in a reference text.