Speaker normalization and speaker adaptation - a combination for conversational speech recognition

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.

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