Abstract
This paper explores the application of information theoretic based Vector Quantization algorithm called VQIT for speaker verification. Unlike the K-means and LBG Vector Quantization algorithms, VQIT has a physical interpretation and relies on minimization of quantization error in an efficient way. Vector Quantization based Speaker Verification has proven to be successful; usually a codebook is trained to minimize the quantization error for the data from an individual speaker. In this paper we use a set of 36 speakers from TIMIT database and evaluate MFCC and LPC coefficients of speech samples and later apply it to the K-means Vector Quantization, LBG Vector Quantization and VQIT Vector Quantization and suggest that VQIT performs better than other VQ implementations. We also obtain the results from the GMM classifier for the similar coefficient data and compare it to the VQIT.
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Memon, S., Lech, M. (2008). Speaker Verification Based on Information Theoretic Vector Quantization. In: Hussain, D.M.A., Rajput, A.Q.K., Chowdhry, B.S., Gee, Q. (eds) Wireless Networks, Information Processing and Systems. IMTIC 2008. Communications in Computer and Information Science, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89853-5_42
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DOI: https://doi.org/10.1007/978-3-540-89853-5_42
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