Use of fuzzy min-max neural network for speaker identification

Abstract

This paper presents the use of fuzzy min-max neural network for the text independent speaker identification. The fuzzy min-max neural network utilizes fuzzy sets as pattern classes. It is a three layer feedforward network that grows adaptively to meet the demands of the problem. The database containing speech utterances recorded from fifty speakers in Marathi language is used for experimentation. Mel frequency cepstral coefficients that represent short time spectrum are used as features for identification. The results obtained with fuzzy min-max neural network are compared with Gaussian mixture model. It is observed that fuzzy neural network outperforms the Gaussian mixture model and attains the identification accuracy of 99.99 % with 15 second speech utterance.

5 Figures and Tables

Cite this paper

@article{Jawarkar2011UseOF, title={Use of fuzzy min-max neural network for speaker identification}, author={Naresh P. Jawarkar and Ragunath S. Holambe and Tapan Kumar Basu}, journal={2011 International Conference on Recent Trends in Information Technology (ICRTIT)}, year={2011}, pages={178-182} }