Projects per year
Abstract
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 language | English |
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Title of host publication | Interspeech 2004 - ICSLP |
Subtitle of host publication | 8th International Conference on Spoken Language Processing |
Publisher | International Speech Communication Association |
Pages | 1477-1480 |
Number of pages | 4 |
ISBN (Print) | ISSN: 1990-9772 |
Publication status | Published - 1 Sept 2004 |
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Dive into the research topics of 'Articulatory feature recognition using dynamic Bayesian networks'. Together they form a unique fingerprint.Projects
- 3 Finished
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Graphical models for automatic speech recognition
King, S. (Principal Investigator)
1/04/04 → 31/08/04
Project: Research
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ASR using loosely-coupled HMMs with articulatory- acoustic features
King, S. (Principal Investigator)
UK central government bodies/local authorities, health and hospital authorities
1/01/04 → 29/02/12
Project: Research
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Espresso-II: SWITCHING LINEAR DYNAMIC SPEECH MODELS
King, S. (Principal Investigator)
1/07/03 → 30/06/06
Project: Research