Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Speech recognition with dynamic Bayesian networks
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Speech recognition with dynamic bayesian networks
Speech recognition with dynamic bayesian networks
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Dynamic Bayesian networks for audio-visual speech recognition
EURASIP Journal on Applied Signal Processing
The generalized distributive law
IEEE Transactions on Information Theory
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Hidden Markov models (HMMs) are the most commonly used stochastic model encoding acoustic features in speech recognition. The token passing model is an abstract model for HMM-based continuous speech recognition to uncouple acoustic models (HMMs) and the language model. Recently, there has been an increasing interest in a general class of probabilistic models: dynamic Bayesian networks (DBNs). Although a huge success of the introduction of DBNs into speech recognition in many areas, the frameworks and recognition algorithms for DBN-based continuous speech recognition are not as mature and flexible as those for HMM-based one. This paper is trying to propose a general framework to inherit most features of state-of-the-art HMM-based frameworks for continuous speech recognition and incorporate the interpretability, factorization and extensibility of DBNs into our framework. The token passing model is adapted for DBN-based continuous speech recognition to achieve this goal and a novel recognition algorithm independent of the upper-layer language model is proposed in this paper.