Bayesian sensing hidden Markov models for speech recognition

George Saon*, Jen-Tzung Chien

*Corresponding author for this work

研究成果: Conference contribution同行評審

12 引文 斯高帕斯(Scopus)

摘要

We introduce Bayesian sensing hidden Markov models (BS-HMMs) to represent speech data based on a set of state-dependent basis vectors. By incorporating the prior density of sensing weights, the relevance of a feature vector to different bases is determined by the corresponding precision parameters. The BS-HMM parameters, consisting of the basis vectors, the precision matrices of sensing weights and the precision matrices of reconstruction errors, are jointly estimated by maximizing the likelihood function, which is marginalized over the weight priors. We derive recursive solutions for the three parameters, which are expressed via maximum a posteriori estimates of the sensing weights. Experimental results on an LVCSR task show consistent gains over conventional HMMs with Gaussian mixture models for both ML and discriminative training scenarios.

原文English
主出版物標題2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
頁面5056-5059
頁數4
DOIs
出版狀態Published - 18 八月 2011
事件36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
持續時間: 22 五月 201127 五月 2011

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(列印)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
國家Czech Republic
城市Prague
期間22/05/1127/05/11

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