Mixtures of Bayesian Joint Factor Analyzers for Noise Robust Automatic Speech Recognition

被引：0
|
作者：
Cui, Xiaodong [1 ]
Goel, Vaibhava [1 ]
Kingsbury, Brian [1 ]
机构：
[1] IBM T J Watson Res Ctr, Yorktown Hts, NY 10598 USA
来源：
14TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2013), VOLS 1-5 | 2013年
关键词：
Bayesian joint factor analysis; automatic relevance determination; relevance vector machine; noise robustness; LVCSR; SPEAKER; VARIABILITY;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper investigates a noise robust approach to automatic speech recognition based on a mixture of Bayesian joint factor analyzers. In this approach, noisy features are modeled by two joint groups of factors accounting for speaker and noise variabilities which are estimated by clean and noisy speech respectively. The factors form an overcomplete dictionary with a redundant representation. Automatic relevance determination (ARD) is carried out by the relevance vector machine (RVM) where sparsity-promoting priors are applied on two factor loading matrices. Experiments on large vocabulary continuous speech recognition (LVCSR) tasks show good improvements by this approach.
引用
收藏
页码：3011 / 3015
页数：5
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