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.

In recent years, the number of studies investigating new directions in speech modeling that goes beyond the conventional HMM has increased considerably. One promising approach is to use Bayesian Networks (BN) as speech models. Full recognition systems based on Dynamic BN as well as acoustic models using BN have been proposed lately. Our group at ATR has been developing a hybrid HMM/BN model, which is an HMM where the state probability distribution is modeled by a BN, instead of commonly used mixtures of Gaussian functions. In this paper, we describe how to use the hybrid HMM/BN acoustic models, especially emphasizing some design and implementation issues. The most essential part of HMM/BN model building is the choice of the state BN topology. As it is manually chosen, there are some factors that should be considered in this process. They include, but are not limited to, the type of data, the task and the available additional information. When context-dependent models are used, the state-level structure can be obtained by traditional methods. The HMM/BN parameter learning is based on the Viterbi training paradigm and consists of two alternating steps - BN training and HMM transition updates. For recognition, in some cases, BN inference is computationally equivalent to a mixture of Gaussians, which allows HMM/BN model to be used in existing decoders without any modification. We present two examples of HMM/BN model applications in speech recognition systems. Evaluations under various conditions and for different tasks showed that the HMM/BN model gives consistently better performance than the conventional HMM.