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Towards Optimal Bayes Decision for Speech Recognition

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4 Author(s)
Jen-Tzung Chien ; Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan ; Huang, Chih-Hsien ; Shinoda, K. ; Furui, S.

This paper presents a new speech recognition framework towards fulfilling optimal Bayes decision theory, which is essential for general pattern recognition. The recognition procedure is developed through minimizing the Bayes risk, or equivalently the expected loss due to classification action. Typically, loss function measures the penalty/evidence of choosing a candidate hypothesis. This function was manually specified or empirically calculated. Here, we exploit a novel Bayes loss function via testing the hypotheses whether the classification action produces loss or not. A Bayes factor is derived to measure loss in a statistical and meaningful way. Attractively, Bayes loss function using predictive distributions is robust to the uncertainty of environments. Also, optimizing this Bayes criterion equals to minimizing classification errors of test data. The relation between the minimum classification error (MCE) classifier and the proposed optimal Bayes classifier (OBC) is bridged. Specifically, the logarithm of Bayes factor in OBC is analogous to the misclassification measure in MCE when using predictive distribution as the discriminant function. We accordingly build a robust and discriminative classification for large vocabulary continuous speech recognition. In the experiments on broadcast news transcription, the new OBC rule significantly outperforms traditional maximum a posteriori classification

Published in:

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on  (Volume:1 )

Date of Conference:

14-19 May 2006

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