Speaker recognition and verification using artificial neural network

Speaker recognition is a biometrie technique which uses individual voice samples for recognition purpose. Speaker recognition is mainly divided into speaker identification and speaker verification. In this paper, a comparative study is made between various combinations of features for speaker identification. Mel frequency Cepstral Coefficient (MFCC) features are combined with spectral centroid and spectral subtraction and tested for improvement in efficiency. Feed forward artificial neural network is used as a classifier. System was tested for 30 speakers. For speaker identification, an average identification rate of 65.3% is achieved when MFCC is combined with centroid features and an identification rate of 60% is achieved when MFCC is combined with spectral subtraction. For speaker verification, an average verification rate of 65.7% is achieved when MFCC is combined with spectral subtraction and a verification rate of 75.3% is achieved when MFCC is used along with centroid.