Malay language text-independent speaker verification using NN-MLP classifier with MFCC

Speaker Recognition (SP) is a topic of great significance in areas of intelligent and security. In Biometric SP using automated method of verifying or recognizing the identity of the person. SP can divide into two: speaker identification and verification. In this paper we focus on speaker verification in Malay language. Speaker Verification (SV) is the task of automatically accepting or rejecting a claimed identity based on the voice characteristics of a speaker. Speaker verification can be divided into text-dependent and text-independent. In this paper, we study the applicability of Artificial Neural Network (ANNs) as core classifiers for Mel Frequency Cepstral Coefficients (MFCC). We also applied a sampled method for speaker recognition that is based on ANNs. The experiment result shows that the MLP achieved highest accuracy, the Artificial Neural Network show better performance for speech and need less training data.