Text-dependent speaker identification using hidden Markov Model with stress compensation technique

In this paper, we present an algorithm for an isolated-word text-dependent speaker identification under normal and four stressful styles. The styles which are designed to simulate speech produced under real stressful conditions are : shout, slow, loud, and soft. The algorithm is based on hidden Markov model (HMM) with cepstral stress compensation technique. Comparing HMM without cepstral stress compensation technique with HMM combined with cepstral stress compensation technique, the recognition rate has improved with a little increase in the computations. The recognition rate has improved : from 90 % to 93 % in normal style, from 19 % to 73 % in shout style, from 62 % to 84 % in slow style, from 38 % to 75 % in loud style, and from 30 % to 81 % in soft style. Cepstral coefficients and transitional coefficients are combined to form an observation vector of hidden Markov model. This algorithm is tested on a limited number of speakers due to our limited data base.