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

• Published in: 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) Vol. 1; pp. 329 - 332 vol.1
• Main Authors: Demirekler, M., Haydar, A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 1999
• Subjects: Cepstral analysis; Covariance matrix; Gaussian distribution; Genetic algorithms; Impedance matching; Linear predictive coding; Speaker recognition; Speech; Testing; Training data
• ISBN: 0780350413; 9780780350410
• ISSN: 1520-6149
• DOI: 10.1109/ICASSP.1999.758129

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 mote desirable to use the subset of the complete feature set found using the genetic algorithm suggested.