Vector quantization in language independent speaker identification using mel-frequency cepstrum co-efficient

Abstract

Speaker recognition is a process of recognizing a person based on their unique voice signals and it is a topic of great importance in areas of intelligent and security. Considerable research and development has been carried out to extract speaker specific features and to develop features matching techniques. The goal of this paper is to perform text-independent speaker identification. These models rely on Mel Frequency Cepstral Coefficients (MFCC) for extraction of speaker specific features and for speaker modelling Vector Quantization (VQ) is used due to high accuracy and simplicity. The proposed system efficiency was analyzed by using 20 filter banks for extracting features. The performance was evaluated using MATLAB against different speakers in different languages such as Tamil, Malayalam, Hindi, Telugu and English with duration of 2, 3 and 4 s. Experimental result shows that 4 s duration of speech regardless of language is able to produce 98 %, 99 % and 97 % of identification when compared to 2 and 3 s. The system efficiency may further be improved using other speaker modelling techniques like Neural Network, Hidden Markov Model and Gaussian Mixture Model. © 2014 Springer International Publishing Switzerland.