Speaker Recognition for Hindi Speech Signal using MFCC-GMM Approach

Abstract

Speaker recognition for different languages is still a big challenge for researchers. The accuracy of identification rate (IR) is great issue, if the utterance of speech sample is less. This paper aims to implement speaker recognition for Hindi speech samples using Mel frequency cepestral coffiecient-vector quantization (MFCC-VQ) and Mel frequency cepestral cofficient-Gaussian mixture model (MFCC-GMM) for text dependent and text independent phrases. The accuracy of text independent recognition by MFCC-VQ and MFCC-GMM for Hindi speech sample is 77.64% and 86.27% respectively. However, the accuracy has increased significantly for text dependent recognition. The accuracy of Hindi speech samples are 85.49 % and 94.12 % using MFCC-VQ and MFCC-GMM approach. We have tested 15 speakers consisting 10 male and 5 female speakers. The total number of trails for each speaker is 17.