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
2 MAIA - Autonomous intelligent machine

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.

Mots clés: hmm adaptation; minimum cross-entropy; robust speech recognition; adaptation hmm

Domaines: Autre [cs.OH]

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