The most widely used acoustic unit in current automatic speech recognition systems is the triphone, which includes the immediate preceding and following phonetic contexts. Although triphones have proved to be an efficient choice, it is believed that they are insufficient in capturing all of the coarticulation effects. A wider phonetic context seems to be more appropriate, but often suffers from the data sparsity problem and memory constraints. Therefore, an efficient modeling of wider contexts needs to be addressed to achieve a realistic application for an automatic speech recognition system. This paper presents a new method of modeling pentaphone-context units using the hybrid HMM/BN acoustic modeling framework. Rather than modeling pentaphones explicitly, in this approach the probabilistic dependencies between the triphone context unit and the second preceding/following contexts are incorporated into the triphone state output distributions by means of the BN. The advantages of this approach are that we are able to extend the modeled phonetic context within the triphone framework, and we can use a standard decoding system by assuming the next preceding/following context variables hidden during the recognition. To handle the increased parameter number, tying using knowledge-based phoneme classes and a data-driven clustering method is applied. The evaluation experiments indicate that the proposed model outperforms the standard HMM based triphone model, achieving a 9-10% relative word error rate (WER) reduction.

Over the last decade, the Bayesian approach has increased in popularity in many application areas. It uses a probabilistic framework which encodes our beliefs or actions in situations of uncertainty. Information from several models can also be combined based on the Bayesian framework to achieve better inference and to better account for modeling uncertainty. The approach we adopted here is to utilize the benefits of the Bayesian framework to improve acoustic model precision in speech recognition systems, which modeling a wider-than-triphone context by approximating it using several less context-dependent models. Such a composition was developed in order to avoid the crucial problem of limited training data and to reduce the model complexity. To enhance the model reliability due to unseen contexts and limited training data, flooring and smoothing techniques are applied. Experimental results show that the proposed Bayesian pentaphone model improves word accuracy in comparison with the standard triphone model.