Gender classification in two emotional speech database
File(s)
Accepted version
Author(s)
Kotti, Margarita
Kotropoulos, Constantine
Type
Conference Paper
Abstract
Gender classification is a challenging problem, which finds applications in speaker indexing, speaker recognition, speaker diarization, annotation and retrieval of multimedia databases, voice synthesis, smart human-computer interaction, biometrics, social robots etc. Although it has been studied for more than thirty years, by no means it is a solved problem. Processing emotional speech in order to identify speakers gender makes the problem even more interesting. A large pool of 1379 features is created including 605 novel features. A branch and bound feature selection algorithm is applied to select a subset of 15 features among the 1379 originally extracted. Support vector machines with various kernels are tested as gender classifiers, when applied to two databases, namely: the Berlin database of Emotional Speech and the Danish Emotional Speech database. The reported classification results outperformthose obtained by state-of-the-art techniques, since a perfect classification accuracy is obtained. © 2008 IEEE.
Date Issued
2008-12
Citation
19th International Conference on Pattern Recognition, 2008, pp.1-4
ISBN
978-1-4244-2174-9
ISSN
1051-4651
Publisher
IEEE
Start Page
1
End Page
4
Journal / Book Title
19th International Conference on Pattern Recognition
Copyright Statement
© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
ICPR 2008
Source Place
Tampa, USA
Start Date
2008-12-08
Finish Date
2008-12-11
Coverage Spatial
Tampa, USA