Effect of MFCC normalization on vector quantization based speaker identification - Shirali-Shahreza, M H; Shirali-Shahreza, Sajad (The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010)
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.