Text-Independent Speaker Identification Using Hidden Markov Models
Mangesh Sudhir Deshpande, Raghunath Sambhaji Holambe · 2008
This paper presents a closed-set, text-independent speaker identification using continuous density hidden Markov model (CDHMM). Each registered speaker has a separate HMM which is trained using Baum-Welch algorithm. The system performance has been studied for different system parameters such as the number of states, number of mixture components per state and the amount of data required for training. Identification accuracy of 100% is achieved by conducting the experiments on TIMIT database.