Continuous Multi-Band Speech Recognition using Bayesian Networks

Colloque avec actes et comité de lecture. internationale.International audienceUsing the Bayesian networks framework, we present a new multi-band approach for continuous speech recognition. This new approach has the advantage to overcome all the limitations of the standard multi-band techniques. Moreover, it leads to a higher fidelity speech modeling than HMMs. We provide a preliminary evaluation of the performance of our new approach on a connected digits recognition task

A markov random field based multi-band model

ABSTRACT An extension of the multi-band model including inter-band control of time asynchrony is described. It is based on the framework of Markov random o/elds. The law of the speech process is given by a parametric Gibbs distribution and a maximum likelihood parameter estimation algorithm is developed. This random o/eld model is applied to isolated word recognition. It is shown that similar performances are obtained with the new model and with standard HMM techniques in the mono-band case. In the multi-band case, it is shown that the recognition rate decreases when the number of band is increased but that modeling inter-band synchrony limits the performance decrease