Articulatory feature recognition using dynamic Bayesian networks

J. Frankel, M. Wester, S. King

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationInterspeech 2004 - ICSLP
Subtitle of host publication8th International Conference on Spoken Language Processing
PublisherInternational Speech Communication Association
Number of pages4
ISBN (Print)ISSN: 1990-9772
Publication statusPublished - 1 Sep 2004


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