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.

Index Terms—Gaussian mixture models, Bessel functions, text independent, speaker identification.

Shivesh Ranjan is with the Department of E.C.E., Manipal Institute of Technology, Manipal-576104, India (e-mail: vireshranjan@yahoo.co.in)
Viresh Ranjan is with the Department of E.C.E., Birla Institute of Technology, Mesra-835215, India (e-mail: chetana@research.iiit.ac.in)
Suryakanth V. Gangashetty is with International Institute of Information Technology, Hyderabad-500032, India (e-mail: svg@iiit.ac.in)

Cite: Shivesh Ranjan, Viresh Ranjan, Chetana Prakash, and Suryakanth V. Gangashetty, "Text Independent Speaker Identification Using Bessel Features," International Journal of Computer and Electrical Engineering vol. 4, no. 5, pp. 715-717, 2012.