Correction of likelihoods for degrees of freedom in robust speech recognition using missing feature theory

被引：0
|
作者：
Van hamme, H [1 ]
机构：
[1] Katholieke Univ Leuven, Dept ESAT, B-3001 Heverlee, Belgium
来源：
SEVENTH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, VOL 1, PROCEEDINGS | 2003年
关键词：
D O I：
10.1109/ISSPA.2003.1224725
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In Missing Feature Theory (MFT), noise robustness of speech recognizers is obtained by modifying the likelihood computed by the acoustic model to express that some features extracted from the signal are unreliable or missing. In one implementation of MFT, the acoustic model and bounds on the unreliable feature are used to infer an estimate of the missing data. This paper addresses an observed bias of the likelihood evaluated at the estimate. Theoretical and experimental evidence are provided that an upper bound on the accuracy is improved by applying a computationally simple correction for the number of free variables in the likelihood maximization.
引用
收藏
页码：401 / 404
页数：4
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