Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Class-Discriminative Weighted Distortion Measure for VQ-based Speaker Identification
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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The performance of speaker identification systems has improved due to recent advances in speech processing techniques but there is still need of improvement in term of text-independent speaker identification and suitable modelling techniques for voice feature vectors. It becomes difficult for person to recognize a voice when an uncontrollable noise adds in to it. In this paper, feature vectors from speech are extracted by using Mel-Frequency Cepstral Coefficients and Vector Quantization technique is implemented through Linde-Buzo-Gray algorithm. Two purposeful speech databases with added noise, recorded at sampling frequencies 8000 Hz and 11025 Hz, are used to check the accuracy of the developed speaker identification system in non-ideal conditions. An analysis is also provided by performing different experiments on the databases that number of vectors in VQ codebook and sampling frequency influence the identification accuracy significantly.