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A Statistical Acoustic Confusability Metric Between Hidden Markov Models

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2 Author(s)
Hong You ; Dept. of Electr. Eng., California Univ., Los Angeles, CA, USA ; Alwan, A.

With the wide application of hidden Markov models (HMMs) in speech recognition, a statistical acoustic confusability metric 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 order 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 measurement and discuss a cost-efficient implementation of the metric. The proposed approach outperforms existing metrics in predicting phonemic confusions.

Published in:

Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on  (Volume:4 )

Date of Conference:

15-20 April 2007