Dynamic Bayesian networks for Arabic phonemes recognition

The majority of current automatic speech recognition systems uses a probabilistic modeling of the speech signal by hidden Markov models (HMM). In addition, the HMM are just a special case of graphical models which are dynamic Bayesian Networks (DBN). These are modeling tools more sophisticated because they allow to include several specific variables in the problem of automatic speech recognition other than the one used in HMM. The use of DBNs in speech recognition beyond has generated much interest in recent years [1] [2] [3] [4] [5]. This paper describes a brief survey of the use of dynamic Bayesian networks (DBN) for automatic speech recognition and presents the use of the DBN on Arabic phonemes recognition comparing to HMM. The primary motivation of this work is to move away from the limitations of HMM. Performance using DBNs is found to exceed that of HMMs trained on an identical task, giving higher recognition accuracy.