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Speech Recognition with Dynamic Bayesian Networks

Dynamic Bayesian networks (DBNs) are a powerful and flexible methodology for representing and computing with probabilistic models of stochastic processes. In the past decade, there has been increasing interest in applying them to practical problems, and this thesis shows that they can be used effectively in the field of automatic speech recognition. 
A principle characteristic of dynamic Bayesian networks is that they can model an arbitrary set of variables as they evolve over time. Moreover, an arbitrary set of conditional independence assumptions can be specified, and this allows the joint distribution to be represented in a highly factored way. Factorization allows for models with relatively few parameters, and computational efficiency. Standardized inference and learning routines allow a variety of model structures to be tested without deriving new formulae, or writing new code. 
The contribution of this thesis is to show how DBNs can be used in automatic speech recognition. This involves solving problems related to both representation and inference. Representationally, the thesis shows how to encode stochastic finite-state word models as DBNs, and how to construct DBNs that explicitly model the speech-articulators, accent, gender, speaking-rate, and other important phenomena. Technically, the thesis presents inference routines that are especially tailored to the requirements of speech recognition: efficient inference with deterministic constraints, variable-length utterances, and online inference. Finally, the thesis presents experimental results that indicate that real systems can be built, and that modeling important phenomena with DBNs results in higher recognition accuracy.