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Effect of MFCC normalization on vector quantization based speaker identification

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2 Author(s)
Shirali-Shahreza, M.H. ; Virtual Educ. Grad. Coll., Amirkabir Univ. of Technol., Tehran, Iran ; Shirali-Shahreza, S.

Mel Frequency Cepstral Coefficients (MFCC) are widely used in speech recognition and speaker identification. MFCC features are usually pre-processed before being used for recognition. One of these pre-processing is creating delta and delta-delta coefficients and append them to MFCC to create feature vector. Another pre-processing is coefficients mean normalization. In this paper, the effect of these two processes on the accuracy of a Vector Quantization (VQ) speaker identification system is compared. Additionally, it is shown that coefficient variance normalization, which is less common, can improve the accuracy.

Published in:

Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on

Date of Conference:

15-18 Dec. 2010