Malay speaker recognition system based on discrete HMM

This paper presents the design and implementation of Malay speaker recognition system using discrete hidden Markov model (HMM) as the classifier. A series of speaker recognition experiments was performed using 99 speakers (13 clients and 86 imposters) recording database consisting of isolated digit utterances. For a seven digit long sequence, 0.96% EER was achieved.