Text-independent speaker verification based on information theoretic learning | [Mehran University Research Journal of Engineering and Technology • 2011]

Author(s):

1. Sheeraz MEMON: Department of Computer Systems Engineering, Mehran University of Engineering & Technology, Jamshoro, PAKISTAN

2. Tariq Jameel Saifullah KHANZADA: Department of Computer Systems Engineering, Mehran University of Engineering & Technology, Jamshoro, PAKISTAN

3. Sania BHATTI: Department of Software Engineering, Mehran University of Engineering & Technology, Jamshoro, PAKISTAN

Abstract:

In this paper VQ (Vector Quantization) based on information theoretic learning is investigated for the task of text-independent speaker verification. A novel VQ method based on the IT (Information Theoretic) principles is used for the task of speaker verification and compared with two classical VQ approaches: the K-means algorithm and the LBG (Linde Buzo Gray) algorithm. The paper provides a theoretical background of the vector quantization techniques, which is followed by experimental results illustrating their performance. The results demonstrated that the ITVQ (Information Theoretic Vector Quantization) provided the best performance in terms of classification rates, EER (Equal Error Rates) and the MSE (Mean Squared Error) compare to Kmeans and the LBG algorithms. The outstanding performance of the ITVQ algorithm can be attributed to the fact that the IT criteria used by this algorithm provide superior matching between distribution of the original data vectors and the codewords.

Page(s): 457-468

DOI: DOI not available

Published: Journal: Mehran University Research Journal of Engineering and Technology, Volume: 30, Issue: 3, Year: 2011

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