Category: CMUTR
Institution: Department of Computer Science, Carnegie
        Mellon University
Abstract: In a typical speech recognition system, computing the match
        between an incoming acoustic string and many competing models
        is computationally expensive.
        Once the highest ranking models are identified, all other match
        scores are discarded.
        We propose to make use of all computed scores by means of 
        statistical inference.
        We view the match between an incoming acoustic string s and a
        model Mi as a random variable Yi.
        The class-conditional distributions of (Y1,...,YN) can be 
        studied offline by sampling, and then used in a variety of
        ways.
        For example, the means of these distributions give rise to a
        natural measure of distance between models.
        
        One of the most useful applications of these distributions is 
        as a basis for a new Bayesian classifier.
        The latter can be used to significantly reduce search effort
        in large vocabularies, and to quickly obtain a short list of
        candidate words.
        An example HMM-based system shows promising results.
        
        
Number: CMU-CS-91-148
Bibtype: TechReport
Month: May
Author: Ronald Rosenfeld
        Xuedong Huang
        Merrick Furst		
Title: Exploiting Correlations Among Models with Application to 
        Large Vocabulary Speech Recognition
Year: 1991
Address: Pittsburgh, PA
Super: @CMUTR