A Minimum Cross-Entropy Approach to Hidden Markov Model Adaptation
Identifieur interne : 00A711 ( Main/Exploration ); précédent : 00A710; suivant : 00A712A Minimum Cross-Entropy Approach to Hidden Markov Model Adaptation
Auteurs : Mohamed Afify ; Yifan Gong ; Jean-Paul Haton [France]Source :
- IEEE Signal Processing Letters ; 1999.
English descriptors
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.
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Centre national de la recherche scientifique, Institut national de recherche en informatique et en automatique, Laboratoire lorrain de recherche en informatique et ses applications, Université de Lorraine
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