Noisy speech recognition using variance adapted likelihood measure

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

*此作品的通信作者

研究成果: Conference article同行評審

5 引文 斯高帕斯(Scopus)

摘要

Because the norm of testing cepstral vector is shrunk in noisy environment, the model parameters, i.e. mean vector and covariance matrix, should be adapted simultaneously. In this study, we propose a method called variance adapted likelihood measure (VALM) which adapts the mean vector using a projection-based scale factor and adapts the covariance matrix using a variance reduction function estimated from the training database. The variance reduction function can be obtained according to various phonetic units. In the hidden Markov model based experiments, the speech recognition performance is greatly improved by applying VALM. The most significant improvement is achieved when the variance reduction function is separately estimated for different state parameters.

原文English
頁(從 - 到)45-48
頁數4
期刊ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
1
出版狀態Published - 1 一月 1996
事件Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA
持續時間: 7 五月 199610 五月 1996

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