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This paper describes the theory and implementation of dynamic Bayesian networks in the context of speaker identification. Dynamic Bayesian networks provide a succinct and expressive graphical language for factoring joint probability distributions, and we begin by presenting the structures that are appropriate for doing speaker identification in clean and noisy environments. This approach is notable...
This paper describes the theory and implementation of DYNAMIC Bayesian networks in the context of isolated digit recognition. The common statistical model used in isolated digit recognition is the hidden Markov model. Bayesian networks provide an expressive graphical language for factoring joint probability distributions. The principle of this approach is to build a speech model using the formalism...
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