Using adaptive genetic algorithms to improve speech emotion recognition

被引：6

作者：

Sedaaghi, Mohammad H. ^{[1
]}

Kotropoulos, Constantine ^{[2
]}

Ververidis, Dimitrios ^{[2
]}

机构：

[1] Sahand Univ Technol, Dept Elect Engn, Tabriz, Iran

[2] Aristotle Univ Thessaloniki, Dept Informat, Thessaloniki 54124, Greece

来源：

2007 IEEE NINTH WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING | 2007年

关键词：

D O I：

10.1109/MMSP.2007.4412916

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, adaptive genetic algorithms are employed to search for the worst performing features with respect to the probability of correct classification achieved by the Bayes classifier in a first stage. These features are subsequently excluded from sequential floating feature selection that employs the probability of correct classification of the Bayes classifier as criterion. In a second stage, adaptive genetic algorithms search for the worst performing utterances with respect to the same criterion. The sequential application of both stages is demonstrated to improve speech emotion recognition on the Danish Emotional Speech database.

引用

页码：461 / +

页数：2

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