Un calcul de Viterbi pour un Modèle de Markov Caché Contraint

Matthieu Petit, Henning Christiansen

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A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with hidden states. This model has been widely used in speech recognition and biological sequence analysis. Viterbi algorithm has been proposed to compute the most probable value of these hidden states in regards to an observed data sequence. Constrained HMM extends this framework by adding some constraints on a HMM process run.

In this paper, we propose to introduce constrained HMMs into Constraint Programming. We propose new version of the Viterbi algorithm for this new framework. Several constraint techniques are used to reduce the search of the most probable value of hidden states of a constrained HMM. An implementation based on PRISM, a logic programming language for statistical modeling, is presented.

Original languageFrench
Title of host publicationProceedings des 5ème Journée Francophone de Programmation par Contraintes
Publication date2009
Publication statusPublished - 2009
Event5ème Journée Francophone de Programmation par Contraintes - Orléans, France
Duration: 3 Jun 20095 Jun 2009


Conference5ème Journée Francophone de Programmation par Contraintes


  • Constrained Hidden Markov Model
  • Constraint Programming
  • Viterbi Computation

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