Acoustic Model Adaptation Using First-Order Linear Prediction for Reverberant Speech

Authors:
Tetsuya Takiguchi;Masafumi Nishimura;Yasuo Ariki
Affiliations:
The authors are with the IBM Tokyo Research Laboratory, Yamato-shi, 242--8502 Japan. E-mail: takigu@kobe-u.ac.jp,;The authors are with the IBM Tokyo Research Laboratory, Yamato-shi, 242--8502 Japan. E-mail: takigu@kobe-u.ac.jp,;The author is with the Department of Computer and System Engineering, Kobe University, Kobe-shi, 657--8501 Japan.,
Venue:
IEICE - Transactions on Information and Systems
Year:
2006

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Reverberation model-based decoding in the logmelspec domain for robust distant-talking speech recognition

IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications

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Abstract

This paper describes a hands-free speech recognition technique based on acoustic model adaptation to reverberant speech. In hands-free speech recognition, the recognition accuracy is degraded by reverberation, since each segment of speech is affected by the reflection energy of the preceding segment. To compensate for the reflection signal we introduce a frame-by-frame adaptation method adding the reflection signal to the means of the acoustic model. The reflection signal is approximated by a first-order linear prediction from the observation signal at the preceding frame, and the linear prediction coefficient is estimated with a maximum likelihood method by using the EM algorithm, which maximizes the likelihood of the adaptation data. Its effectiveness is confirmed by word recognition experiments on reverberant speech.