Speaker Normalization and Speaker Adaptation - a Combination for Conversational Speech Recognition (1997)
| Venue: | Proceedings of Eurospeech Conference |
| Citations: | 8 - 0 self |
BibTeX
@INPROCEEDINGS{Zhan97speakernormalization,
author = {Puming Zhan and Martin Westphal and Michael Finke and Alex Waibel},
title = {Speaker Normalization and Speaker Adaptation - a Combination for Conversational Speech Recognition},
booktitle = {Proceedings of Eurospeech Conference},
year = {1997},
pages = {2087--2090}
}
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Abstract
Speaker normalization and speaker adaptation are two strategies to tackle the variations from speaker, channel, and environment. The vocal tract length normalization (VTLN) is an e ective speaker normalization approach to compensate for the variations of vocal tract shapes. The Maximum Likelihood Linear Regression(MLLR) is a recent proposed method for speaker-adaptation. In this paper, we propose a speaker-speci c Bark scale VTLN method, investigate the combination of the VTLN with MLLR, and present an iterative procedure for decoding the combined system of VTLN and MLLR. The results show that: (1) the new VTLN method is very e ective with which the word error rate can be reduced up to 11%; (2) the combination of VTLN and MLLR can provide up to 15 % word error reduction; (3) both VTLN and MLLR are more e ective for the push-to-talk data than for the cross-talk data. 1
