A novel projection-based likelihood measure for noisy speech recognition

Jen-Tzung Chien*, Hsiao Chuan Wang, Lee Min Lee

*此作品的通信作者

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

The projection-based likelihood measure, an effective means of reducing noise contamination in speech recognition, dynamically searches an optimal equalization factor for adapting the cepstral mean vector of hidden Markov model (HMM) to equalize the noisy observation. In this paper, we present a novel likelihood measure which extends the adaptation mechanism to the shrinkage of covariance matrix and the adaptation bias of mean vector. A set of adaptation functions is proposed for obtaining the compensation factors. Experiments indicate that the likelihood measure proposed herein can markedly elevate the recognition accuracy.

原文English
頁(從 - 到)287-297
頁數11
期刊Speech Communication
24
發行號4
DOIs
出版狀態Published - 1 一月 1998

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