Estimating Velum Height from Acoustics During Continuous Speech

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper reports on present work, in which a recurrent neural network is trained to estimate 'velum height' during continuous speech. Parallel acoustic-articulatory data comprising more than 400 read TIMIT sentences is obtained using electromagnetic articulography (EMA). This data is processed and used as training data for a range of neural network sizes. The network demonstrating the highest accuracy is identified. This performance is then evaluated in detail by analysing the network's output for each phonetic segment contained in 50 hand-labelled utterances set aside for testing purposes.
Original languageEnglish
Title of host publicationSixth European Conference on Speech Communication and Technology (EUROSPEECH'99)
PublisherInternational Speech Communication Association
Pages149-152
Number of pages4
Publication statusPublished - 1999

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