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Articulatory feature recognition using dynamic Bayesian networks

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)620-640
Number of pages21
JournalComputer Speech and Language
Volume21
Issue number4
Publication statusPublished - 1 Oct 2007

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 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.

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