Articulatory feature recognition using dynamic Bayesian networks

doi:10.1016/j.csl.2007.03.002

2007-10-01

Joe Frankel *

Mirjam Wester

Simon King

DOI：10.1016/j.csl.2007.03.002

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摘要

En 中文

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. (c) 2007 Published by Elsevier Ltd.

Keyword:

articulatory feature recognition

dynamic Bayesian network

neural network

automatic speech recognition

virtual evidence

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期刊

Computer Speech and Language

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3.4

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