Text-Dependent Multilingual Speaker Identification for Indian Languages Using Artificial Neural Network

In this paper an attempt is made to develop speaker identification system which is used to determine the identity of an unknown speaker among several speakers of known speech characteristics, from a sample of his or her voice. Every speaker has different individual characteristics embedded in his /her speech utterances. These characteristics can be extracted from utterances and different neural network models are used to get the desired results. To evaluate speech characteristics from utterances they are stored in digitized form. Speech features namely LPC, RC, APSD, Number of zero crossing and Formant frequencies are extracted from speech signal and formed speech feature vectors. These data features are fed into Artificial Neural Network using back propagation learning algorithm and clustering algorithm for training and identification processes of different speakers. The database used for this system consists of 20 speakers including both male and female from different parts of India and languages are Hindi, Sanskrit, Punjabi and Telugu. The average identification rate 83.29% is achieved when the network is trained using back propagation algorithm and it is improved by about 9% and reached up to 92.78% when using clustering algorithm.