Christophe Antoine

Dynamic Bayesian networks for multi-band automatic speech recognition

Abstract This paper presents a new approach to multi-band automatic speech recognition which has the advantage to overcome many limitations of classical muti-band systems. The principle of this new approach is to build a speech model in the time–frequency domain using the formalism of dynamic Bayesian networks. In contrast to classical multi-band modeling, this formalism leads to a probabilistic speech model which allows communications between the different sub-bands and, consequently, no recombination step is required in recognition. We develop efficient learning and decoding algorithms both for isolated and continuous speech recognition. We present illustrative experiments on isolated and connected digit recognition tasks. These experiments show that the this new approach is very promising in the field of noisy speech recognition.

Continuous multi-band speech recognition using Bayesian networks

Using the Bayesian networks framework, we present a new multi-band approach for continuous speech recognition. This new approach has the advantage of overcoming 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.