Model Adaptation based on HMM decomposition for Reverberant Speech Recognition


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Title:	Model Adaptation based on HMM decomposition for Reverberant Speech Recognition
Authors:	Tetsuya Takiguchi Satoshi Nakamura Qiang Huo Kiyohiro Shikano
Issue Date:	Apr-1997
Publisher:	IEEE
Start page:	827
End page:	830
Abstract:	The performance of a speech recognizer is degraded drastically in reverberant environments. The authors propose a novel algorithm which can model an observation signal by composition of HMMs of clean speech, noise and an acoustic transfer function. However, estimating HMM parameters of the acoustic transfer function is still a serious problem. In their previous paper, they measured real impulse responses of training positions in an experiment room. It is inconvenient and unrealistic to measure impulse responses for every possible new experiment room. The paper presents a new method for estimating HMM parameters of the acoustic transfer function from some adaptation data by using an HMM decomposition algorithm which is an inverse process of the HMM composition. Its effectiveness is confirmed by a series of speaker dependent and independent word recognition experiments on simulated distant-talking speech data
Description:	ICASSP1997: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 21-24, 1997.
URI:	http://hdl.handle.net/10061/8025
ISBN:	0818679190
ISSN:	1520-6149
Rights:	Copyright 1997 IEEE
Text Version:	Publisher
Publisher DOI:	10.1109/ICASSP.1997.596060
Appears in Collections:	情報科学研究科 / Graduate School of Information Science

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