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Abstract / Description of output
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 stateof- 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.
|Number of pages||21|
|Journal||Computer Speech and Language|
|Publication status||Published - 1 Oct 2007|
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- 3 Finished
1/01/05 → 31/12/09