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

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Abstract

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 the hidden Markov model (HMM) with a cepstral stress compensation technique. Comparing the HMM without cepstral stress compensation with the HMM combined with cepstral stress compensation, 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. The cepstral coefficients and transitional coefficients are combined to form an observation vector of the hidden Markov model. This algorithm is tested on a limited number of speakers due to our limited data base.

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