Short note on two output-dependent hidden Markov models

Authors:
Jing-Hao Xue;D. Michael Titterington
Affiliations:
Department of Statistics, University of Glasgow, Glasgow G12 8QQ, UK;Department of Statistics, University of Glasgow, Glasgow G12 8QQ, UK
Venue:
Pattern Recognition Letters
Year:
2008

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Hidden Markov models with states depending on observations

Pattern Recognition Letters
Direct modelling of output context dependence in discriminative hidden Markov model

Pattern Recognition Letters

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Abstract

The purpose of this note is to study the assumption of ''mutual information independence'', which is used by Zhou [Zhou, G.D., 2005. Direct modelling of output context dependence in discriminative hidden Markov model. Pattern Recognition Lett. 26 (5), 545-553] for deriving an output-dependent hidden Markov model, the so-called discriminative HMM (D-HMM), in the context of determining a stochastic optimal sequence of hidden states. The assumption is extended to derive its generative counterpart, the G-HMM. In addition, state-dependent representations for two output-dependent HMMs, namely HMMSDO [Li, Y., 2005. Hidden Markov models with states depending on observations. Pattern Recognition Lett. 26 (7), 977-984] and D-HMM, are presented.