Feature Extraction and Classification Techniques for Speaker Recognition: A Review

被引:0
作者
Dhameliya, Kinnal [1 ]
Bhatt, Ninad [2 ]
机构
[1] CG Patel Inst Technol, Elect & Commun Dept, Surat, India
[2] CK Pithawalla Coll Engn & Technol, Elect & Commun Dept, Surat, India
来源
2015 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, SIGNALS, COMMUNICATION AND OPTIMIZATION (EESCO) | 2015年
关键词
Speaker Recognition; Mel frequency Cepstral Co-efficient (MFCC); Linear Predictive Coding (LPC); Gaussian Mixture Model (GMM); Artificial Neural Network (ANN);
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In recent scenario speaker recognition is used in many places for security purpose. Speaker recognition is technique which can verify or identify the person who is speaking. It is different than speech recognition system. In this paper we have discussed the feature extraction techniques like Mel Frequency Cepstral Co-efficient (MFCC), Linear Predictive Coding (LPC) etc. and classification techniques like Gaussian Mixture Model (GMM), Artificial Neural Networks (ANN) etc. that is available for speaker recognition. Also the survey is about how to get better efficiency in terms of speaker recognition rate by simply modifying the existing feature extraction and classification techniques.
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页数:4
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