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Minimum Word Classification Error Training of HMMs for Automatic Speech Recognition

Zhi-Jie Yan, Bo Zhu, Yu Hu, and Ren-Hua Wang

Abstract

This paper presents a novel discriminative training criterion, minimum word classification error (MWCE). By localizing conventional string-level MCE loss function to word-level, a more direct measure of empirical word classification error is approximated and minimized. Because the word-level criterion better matches performance evaluation criteria such as WER, an improved word recognition performance can be achieved. We evaluated and compared MWCE criterion in a unified DT framework, with other commonly-used criteria including MCE, MMI, MWE, and MPE. Experiments on TIMIT and WS JO evaluation tasks suggest that word-level MWCE criterion can achieve consistently better results than string-level MCE. MWCE even outperforms other substring-level criteria on the above two tasks, including MWE and MPE.

Details

Publication typeInproceedings
Published inIEEE International Conference on Acoustics, Speech and Signal Processing, 2008, ICASSP 2008
URLhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4518661&isnumber=4517521
Pages4521-4524
SeriesICASSP 2008
PublisherIEEE
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