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A STATISTICAL ACOUSTIC CONFUSABILITY METRIC BETWEEN HIDDEN MARKOV Hong You and Abeer Alwan
 

Summary: A STATISTICAL ACOUSTIC CONFUSABILITY METRIC BETWEEN HIDDEN MARKOV
MODELS
Hong You and Abeer Alwan
Department of Electrical Engineering
University of California, Los Angeles, CA 90095
Email: hyou@icsl.ucla.edu, alwan@ee.ucla.edu
ABSTRACT
With the wide application of Hidden Markov Models (HMMs)
in speech recognition, a statistical acoustic confusability met-
ric is of increasing importance to many components of a speech
recognition system. Although distance metrics between HMMs
have been studied in the past, they didn't include a way of
accounting for speaking rate and durational variations. In or-
der to account for the underlying speech signal's properties
when computing such a metric between HMMs, we propose a
dynamically-aligned Kullback Leibler (KL) divergence mea-
surement and discuss a cost-efficient implementation of the
metric. The proposed approach outperforms existing metrics
in predicting phonemic confusions.
Index Terms-- Hidden Markov Models, Speech recogni-

  

Source: Alwan, Abeer - Electrical Engineering Department, University of California at Los Angeles

 

Collections: Computer Technologies and Information Sciences