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Speaker time-drifting adaptation using trajectory mixture hidden Markov models

In this paper, a trajectory mixture hidden Markov model (TMHMM) is proposed to cope with the problems of trajectory variability and speaker time-drifting. A theoretical formulation of TMHMM learning is presented. By introducing two pragmatic adaptation schemes, the practical issues which demonstrate the use of the model in capturing the time-drifting of speaker model for speaker recognition are addressed. To evaluate with the YOHO corpus, a set of phonetic units is defined. The effectiveness of the modeling approach is confirmed by a set of experiments. It is shown that an error rate of 0.07% is obtained for closed-set speaker recognition with a total population of 138 talkers. TMHMM can be considered as a special HMM topology dedicated to the time-drifting adaptation problem.