in recent years, various discriminative learning

techniques for HMMs have consistently yielded significant

benefits in speech recognition. In this paper, we present a novel

optimization technique using the Minimum Classification Error

(MCE) criterion to optimize the HMM parameters. Unlike

Maximum Mutual Information training where an Extended

Baum-Welch (EBW) algorithm exists to optimize its objective

function, for MCE training the original EBW algorithm cannot

be directly applied. In this work, we extend the original EBW

algorithm and derive a novel method for MCE-based model

parameter estimation. Compared with conventional gradient

descent methods for MCE learning, the proposed method gives a

solid theoretical basis, stable convergence, and it is well suited for

the large-scale batch-mode training process essential in largescale

speech recognition and other pattern recognition

applications. Evaluation experiments, including model training

and speech recognition, are reported on both a small vocabulary

task (TI-Digits) and a large vocabulary task (WSJ), where the

effectiveness of the proposed method is demonstrated. We expect

new future applications and success of this novel learning method

in general pattern recognition and multimedia processing, in

addition to speech and audio processing applications we present

in this paper.

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In  Proc. IEEE Workshop on Multimedia Signal Processing

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