论文信息 - On the complexity of explicit duration HMM's

On the complexity of explicit duration HMM's

Introduces a new recursion that reduces the complexity of training a semi-Markov model with continuous output distributions. It is shown that the cost of training is proportional to M/sup 2/+D, compared to M/sup 2/D with the standard recursion, where M is the observation vector length and D is the maximum allowed duration. >

Mary P. Harper | Leah H. Jamieson | C. D. Mitchell

[1] Mary P. Harper,et al. A Parallel Implementation of a Hidden Markov Model with Duration Modeling for Speech Recognition , 1995 .

[2] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[3] Stephen E. Levinson,et al. Continuously variable duration hidden Markov models for automatic speech recognition , 1986 .

[4] Jay G. Wilpon,et al. Modeling state durations in hidden Markov models for automatic speech recognition , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[5] Leah H. Jamieson,et al. Modeling duration in a hidden Markov model with the exponential family , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.