Applying discretized articulatory knowledge to dysarthric speech

Authors:
Frank Rudzicz
Affiliations:
University of Toronto, Department of Computer Science, Ontario, Canada M5S 3G4
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

Citing 0
Cited 3

Correcting errors in speech recognition with articulatory dynamics

ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Towards a noisy-channel model of dysarthria in speech recognition

SLPAT '10 Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Processing for Assistive Technologies
The TORGO database of acoustic and articulatory speech from speakers with dysarthria

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Abstract

This paper applies two dynamic Bayes networks that include theoretical and measured kinematic features of the vocal tract, respectively, to the task of labeling phoneme sequences in unsegmented dysarthric speech. Speaker dependent and adaptive versions of these models are compared against two acoustic-only baselines, namely a hidden Markov model and a latent dynamic conditional random field. Both theoretical and kinematic models of the vocal tract perform admirably on speaker-dependent speech, and we show that the statistics of the latter are not necessarily transferable between speakers during adaptation.