Multistage VQ Based GMM For Text Independent Speaker identification System
Piyush Lotia1, M.R. Khan2
1Piyush Lotia, Associate Professor, Department of Electronics and Telecommunication, SSCET-Bhilai (C.G.), India.
2Dr. M.R. Khan, Associate Professor, Department of Electronics and Telecommunication, GEC Raipur (C.G.), India.
Manuscript received on April 19, 2011. | Revised Manuscript received on April 29, 2011. | Manuscript published on May 05, 2011. | PP: 21-26 | Volume-1 Issue-2, May 2011. | Retrieval Number: A028031211
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The use of Gaussian Mixture Models (GMM) are most common in speaker identification due to it can be performed in a completely text independent situation. However, it sounds efficient to speaker identification application, but it results long time processing in practice. In this paper, we propose a decision function by using vector quantization (VQ)techniques to decrease the training model for GMM in order to reduce the processing time. In our proposed modeling, we take the superiority of VQ, which is simplicity computation to distinguish between male and female speaker. Then, in second phase of classification, decision tree rule are applied to separate out the similar speaker in same gender into two difference group. While in phase 3, GMM is applied into the subgroup of speaker to get the accuracy rates. Experimental result shows that our hybrid VQ/GMM method always yielded better improvements in accuracy and bring almost 20% reduce in time processing.
Keywords: MFCC, VQ, Cepstrum, LBG Algorithim.