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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 compared to those found using artificial neural networks on an identical task.
|Title of host publication||Interspeech 2004 - ICSLP|
|Subtitle of host publication||8th International Conference on Spoken Language Processing|
|Publisher||International Speech Communication Association|
|Number of pages||4|
|ISBN (Print)||ISSN: 1990-9772|
|Publication status||Published - 1 Sep 2004|
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- 3 Finished
Graphical models for automatic speech recognition
1/04/04 → 31/08/04
Espresso-II: SWITCHING LINEAR DYNAMIC SPEECH MODELS
1/07/03 → 30/06/06