Automatic speaker verification experiments using HMM

This paper addresses the design and implementation of automatic speaker verification (ASV) systems. There is great interest in developing and increasing the performance of ASV applications, taking into account the advantages offered when compared to other biometrical methods. State-of-the-art speaker recognizers are based on statistical models such as GMM, HMM, SVM, ANN or hybrid models. This work reports experiments on prompted text speaker verification on a Romanian corpus. First, a Hidden Markov Model (HMM) based continuous speech recognizer is built at context independent, single mixture, monophone level. Then the client model and the world model are built using appropriate speech data. The text-prompted speaker verification system is based on sentence HMMs, constructed for the key text by concatenating corresponding acoustic models. In the verification stage, the normalized log-likelihood is derived as the difference between the log-likelihood obtained through Viterbi forced alignment of the client model and world model, respectively. Finally a procedure used to determine the verification performances is presented.