Abstract—In this paper, we explore the use of Bessel features derived from speech utterances, to develop Gaussian mixture speaker models for text independent Speaker Identification. The proposed approach to speaker identification is based on existing methods that employ Gaussian mixtures for the modeling of speakers. However, we have developed the speaker models from the Bessel features derived from the speech utterances, as an alternative to Mel-frequency cepstral coefficients for developing the speaker models. The proposed approach is tested on two databases of ten and twenty speakers respectively and their performance is evaluated. Finally, we have made some suggestions for future work involving the use of Bessel features for text independent speaker identification and verification.

Index Terms—Bessel functions, Gaussian mixture models,Text independent, Speaker identification.

Mayank Gupta is with National Institute of Technology, Hamirpur (H.P.)India in Electronics & Communication Engineering, India (emailmayank01.gupta@gmail.com).
Suryakanth V. Gangashetty is with Language Technolgy Research Centerin International Institute of Information Technology, Hyderabad, India.(email- svg@iiit.ac.in)

Cite: Mayank Gupta and Suryakanth V. Gangashetty, "Text Independent Speaker Identification and Verification System Using Bessel Featuress," International Journal of Computer and Communication Engineering vol. 2, no. 2, pp. 167-169 , 2013.