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A Minimum Cross-Entropy Approach to Hidden Markov Model Adaptation

Mohamed Afify 1 Yifan Gong Jean-Paul Haton 2
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : An adaptation algorithm using the theoretically optimal maximum a posteriori (MAP) formulation, and at the same time accounting for parameter correlation between different classes is desirables, especially when using sparse adaptation data. However, a direct implementation of such an approach may be prohibitive in many practical situations. In this letter, we present an algorithm that approximates the above mentioned correlated MAP algorithm by iteratively maximizing the set of posterior marginals. With some simplifying assumptions, expression for these marginals are then derived, using the principle of minimum cross-entropy. The resulting algorithm is simple, and includes conventional MAP estimation as a special case. The utility of the proposed method is tested in adaptation experiments for an alphabet recognition task.
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https://hal.inria.fr/inria-00098969
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Submitted on : Tuesday, September 26, 2006 - 8:40:56 AM
Last modification on : Friday, February 26, 2021 - 3:28:06 PM

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  • HAL Id : inria-00098969, version 1

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Mohamed Afify, Yifan Gong, Jean-Paul Haton. A Minimum Cross-Entropy Approach to Hidden Markov Model Adaptation. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 1999, 6 (6), pp.132-134. ⟨inria-00098969⟩

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