Articulatory Feature Recognition Using Dynamic Bayesian Networks
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This paper describes the use of dynamic Bayesian networks for the task of articulatory feature recognition. We show that by modeling the dependencies between a set of 6 multi-leveled articulatory features, recognition accuracy is increased over an equivalent system in which features are considered independent. Results are comparedto those found using artificial neural networks on an identical task.