research-article

A local time Fukunage-Koontz transformation for speech emotional recognition

Authors:

Zhao Yan,

Wang Hai-xian,

Zhao LiAuthors Info & Claims

ICAIT '08: Proceedings of the 2008 International Conference on Advanced Infocomm Technology

Article No.: 116, Pages 1 - 5

https://doi.org/10.1145/1509315.1509431

Published: 29 July 2008 Publication History

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Abstract

Dimension reduction is widely used in the domains of speech emotional recognition. Considering the underlying time structure of speech signals, we propose a local time Fukunage-koontz transformation (LTFKT) to contain more discriminative information during emotional recognition. The goal of LTFKT is to maximize/ minimize the eigenvalues of covariance in different classes simultaneously. It can be seen that FKT is a special case of LTFKT from analysis. The results show that the method in this paper can improve recognition rate effectively.

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Xiaoming Huo. A statistical analysis of Fukunaga-Koontz transform. IEEE, Signal Processing Letters, Vol. 11, No. 2, Part 1, pp.:123--126, 2004.

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K. Fukunaga. Introduction to Statistical Pattern Recognition. 2rd Edition. Academic Press, 1990.

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Published In

ICAIT '08: Proceedings of the 2008 International Conference on Advanced Infocomm Technology

July 2008

677 pages

ISBN:9781605580883

DOI:10.1145/1509315

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 July 2008

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ICAIT '08 Paper Acceptance Rate 89 of 151 submissions, 59%;

Overall Acceptance Rate 122 of 207 submissions, 59%

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