5th European Conference on Speech Communication and Technology

Rhodes, Greece
September 22-25, 1997

Using Simulated Annealing Expectation Maximization Algorithm for Hidden Markov Model Parameters Estimation

Jacques Simonin, Chafic Mokbel

France Telecom - CNET - LAA/TSS/RCP, Lannion, France

This paper presents the use of a simulated annealing technique during the parameters estimation of a Hidden Markov Model (HMM) in a speech recognition system. This technique allows to move out of a local optimum which characterizes a classical Expectation Maximization (EM) algorithm, and thus to achieve a better estimation with a limited amount of training data. We choose here the Simulated Annealing Expectation Maximization (SAEM) algorithm introducing a simulated annealing technique in the EM method. The SAEM algorithm is compared to the classical EM algorithm, for both task- independent and task-dependent Viterbi training. The evaluation leads to significant improvement of recognition performances.

Full Paper

Bibliographic reference.  Simonin, Jacques / Mokbel, Chafic (1997): "Using simulated annealing expectation maximization algorithm for hidden Markov model parameters estimation", In EUROSPEECH-1997, 449-452.