Speaker Recognition using fusion of features with Feedforward Artificial Neural Network and Support Vector Machine

Abstract

Speaker recognition is a task of identifying/verifying individual identity with the help of input voice sample. Speaker recognition is further classified into speaker identification (SI) and speaker verification (SV). Unlike other approaches using ELSDSR speech database, which only focuses on speaker identification performance, proposed work also focuses on speaker verification performance results, along with SI performance. Main goal of this research, is to find out best model for speaker identification and speaker verification system for clean speech database. Comparative study is done between performances by various combinations of features, for speaker identification and speaker verification system with Feedforward Artificial Neural Network (FFANN) and Support Vector Machine(SVM) as an classification technique using ELSDSR voice database. Features named as Linear predictive coding (LPC), Mel frequency cepstral coefficient (MFCC) and Perceptual linear prediction (PLP) are used. All features are tested separately and in fusion among each other's with FFANN and SVM classifier on MATLAB software. Also Proposed model results are compared with some famous techniques using same ELSDSR database for speaker identification system. By comparing experimental results of proposed model and others model, it is observed that fusion of different features gives better results and speaker identification accuracy increases to 3%-5% when compared with single feature's result. In addition, proposed method are giving best result of100% accuracy for speaker identification system and 0 equal error rate(EER) value for speaker verification system, when fusion of MFCC, LPC and PLP features are used with ANN and SVM classifier. Therefore, it can be said that fusion of MFCC, LPC and PLP features greatly enhances the speaker recognition system's performance.