Articulatory feature recognition using dynamic Bayesian networks

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 compared to those found using artificial neural networks on an identical task.

• Type: Journal
• Year: 2007
• Where: CSL
• Authors: Joe Frankel, Mirjam Wester, Simon King
• Keywords: Articulatory Feature Recognition, Articulatory Features, Automated Reasoning, CSL 2007, Dynamic Bayesian Networks