Modeling Duration In A Hidden Markov Model With The Exponential Family (1993) [7 citations — 3 self]
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author = {C.D. Mitchell and L.H. Jamieson},
title = {Modeling Duration In A Hidden Markov Model With The Exponential Family},
booktitle = {In Proc. ICASSP},
year = {1993},
pages = {331--334}
}
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Abstract:
Explicit duration modeling has been shown to increase the effectiveness of hidden Markov models in automatic speech recognition. Ferguson found the optimum parameters of the duration model for the case where duration is assumed to be distributed according to a non-parametric probability mass function. Levinson determined the best gamma density to model duration. In this paper, duration is assumed to be modeled by some probability mass function in the exponential family. An iterative procedure for determining the maximum likelihood parameters is presented. Also given is a method for choosing an appropriate member from the exponential family. 1 Introduction The performance of speech recognizers can be improved by accurately modeling the duration of short speech events. Ferguson [1] extended hidden Markov model (HMM) 1 theory to include a non-parametric probability mass function for the duration of each state. The total number of duration parameters for a speech model is typically very...
Citations
| 2372 | A tutorial on hidden Markov Models and selected applications in speech recognition – Rabiner - 1989 |
| 269 | Theory of Point Estimation – Lehmann - 1983 |
| 90 | Continuously variable duration hidden Markov models for automatic speech reco#nition – Levinson - 1986 |
| 24 | Variable duration models for speech – Ferguson - 1980 |
