Feature selection using genetics-based algorithm and its application to speaker identification

Date

1999-03-19

Author

Demirekler, Mübeccel
Haydar, A

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper introduces the use of genetics-based algorithm in the reduction of 24 parameter set (i.e the base set) to a 5,6,7,8 or 10 parameter set, for each speaker in text-independent speaker identification. The feature selection is done by finding the best features that discriminates a person from his/her two closest neighbors. The experimental results show that there is approximately 5% increase in the recognition rate when the reduced set of parameters are used. Also the amount of calculation necessary for speaker recognition using the reduced set of features is much less than the amount of calculation required using the complete feature set in the testing phase. Hence it is more desirable to use the subset of the complete feature set found using the genetic algorithm suggested.

Subject Keywords

Covariance matrix, Cepstral analysis, Training data, Speaker recognition, Testing, Genetic algorithms, Impedance matching, Speech, Linear predictive coding, Gaussian distribution

URI

https://hdl.handle.net/11511/57587

DOI

https://doi.org/10.1109/icassp.1999.758129

Conference Name

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar

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Speaker identification through use of features selected using genetic algorithm Haydar, A; Demirekler, Mübeccel; Yurtseven, MK (Institution of Engineering and Technology (IET), 1998-01-08) The authors introduce the use of a genetic algorithm in the reduction of a 24 parameter (12 LPC derived cepstral coefficients +12 Delta-cepstral coefficients) set to a five, six, seven, eight or ten parameter set, for each speaker in text-independent speaker identification. The experimental results show that there is similar to 5% increase in the recognition rate when the reduced set of parameters is used.
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Citation Formats

M. Demirekler and A. Haydar, “Feature selection using genetics-based algorithm and its application to speaker identification,” presented at the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99, Phoenix, AZ, USA, 1999, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57587.