A Review of Automatic Speaker Recognition System
Tejal Chauhan1, Hemant Soni2, Sameena Zafar3
1Tejal Chauhan, Research Student, E & C, Patel college of Science and Technology, Bhopal, India.
2Hemant Soni, E & C, Patel college of Science and Technology, Bhopal, India.
3Sameena Zafar, E & C, Patel college of Science and Technology, Bhopal, India.
Manuscript received on August 02, 2013. | Revised Manuscript received on August 28, 2013. | Manuscript published on September 05, 2013. | PP: 132-135 | Volume-3, Issue-4, September 2013. | Retrieval Number: D1797093413/2013©BEIESP
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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: In the recent time, person authentication in security systems using biometric technologies has grown rapidly. The voice is a signal of infinite information. Digital signal processes such as Feature Extraction and Feature Matching are introduced to authenticate person in security system. In this paper concept of speaker recognition is discussed. Several methods such as Liner Predictive Coding (LPC), Mel Frequency Cepstral Coefficients (MFCCs) etc. are utilized for feature extraction and methods like Dynamic Time Warping (DTW), Vector Quantization (VQ), Hidden Markov Model (HMM), Gaussian Mixture Models (GMM) etc are used with a view to identify voice signal.
Keywords: LPC, MFCC, DTW, VQ, HMM, GMM.