A New Speaker Recognition System with Combined Feature Extraction Techniques

Abstract

Problem statement: This study introduces a new method for speaker verification system by fusing two different feature extraction methods to improve the recognition accuracy and security. Approach: The proposed system uses Mel frequency cepstral coefficients for speaker identification and Modified MFCC for verification. For speaker modeling vector quantization is used. Results: The proposed system was investigated the effect of the different length segmental feature as well as speaker modeling for speaker recognition. The performance was evaluated against 1000 speakers for 10 different languages with duration of 10 sec for training the system and for testing 5 sec. duration samples were used. Conclusion/Recommendations: Experimental results of the proposed system showed that higher recognition accuracy of 93% is achieved by increasing the number of filter banks used for feature extraction method, more competitive with existing system using vector quantization with lesser computational complexity. The system efficiency may further be improved using other speaker modeling techniques like GMM, HMM.