Constraint satisfaction model for enhancement of evidence in recognition of consonant-vowel utterances

被引：0
|
作者：
Gangashetty, SV [1 ]
Sekhar, CC [1 ]
Yegnanarayana, B [1 ]
机构：
[1] Indian Inst Technol, Dept Comp Sci & Engn, Speech & Vis Lab, Madras 600036, Tamil Nadu, India
来源：
2003 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL II, PROCEEDINGS: SPEECH II; INDUSTRY TECHNOLOGY TRACKS; DESIGN & IMPLEMENTATION OF SIGNAL PROCESSING SYSTEMS; NEURAL NETWORKS FOR SIGNAL PROCESSING | 2003年
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D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
In this paper, we address the issues in recognition of a large number of subword units of speech with high confusability among several units. Evidence available from the classification models trained with a limited number of training examples may not be strong to correctly recognize the subword units. We present a constraint satisfaction neural network model that can be used to enhance the evidence for a particular unit with the supporting evidence available for a subset of units confusable with that unit. We demonstrate the enhancement of evidence by the proposed model in recognition of utterances of 145 consonant-vowel units.
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页码：753 / 756
页数：4
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