Toward Markov random field modeling of speech

In this paper, we present a new technique for statistical modeling of speech segments based on Markov random fields. Classical and multi-stream HMMs are particular cases of this more general family of models. However, the Random Field Model (RFM) proposed here can be seen as an extension of the multiband HMM in which interactions between the frequency bands have been added. In a first experiment, samples are drawn from different models and compared to real observations. This experiment shows that the RFM is able to produce realistic samples but a single HMM still performs better. Isolated word recognition experiments stress the fact that more work must be done on the RFM in order to reach the performances of classical hidden Markov modeling techniques. For the moment, the RFM parameters are estimated using a heuristic. We believe that a real maximum likelihood parameter estimation algorithm should improve the results. The main advantage of this new model is that it can easily be extended since a model is defined by some local interactions and the Gibbs potential functions associated to those interactions.