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Articulatory feature recognition using dynamic Bayesian networks.

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Frankel_etal_CSL2007.pdf (1.700Mb)
Date
2007
Author
Frankel, Joe
Wester, Mirjam
King, Simon
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Abstract
We describe a dynamic Bayesian network for articulatory feature recognition. The model is intended to be a component of a speech recognizer that avoids the problems of conventional ``beads-on-a-string'' phoneme-based models. We demonstrate that the model gives superior recognition of articulatory features from the speech signal compared with a state of- the art neural network system. We also introduce a training algorithm that offers two major advances: it does not require time-aligned feature labels and it allows the model to learn a set of asynchronous feature changes in a data-driven manner.
URI
http://hdl.handle.net/1842/1991
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  • CSTR publications
  • Linguistics and English Language publications

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