Learning and adaptation of a tongue shape modelwith missing data

Using data-driven techniques and ultrasound data, it is possible to learn models that reconstruct the tongue shape of a speaker with submillimetric accuracy given the location of 3-4 fleshpoints, and to adapt these models to a new speaker for which little data is available. In practice, tongue conto...

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Bibliographic Details
Published in2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3981 - 3984
Main Authors Farhadloo, M., Carreira-Perpinan, M. A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2012
Subjects
Adaptation models
articulatory databases
Data models
missing data
model adaptation
Shape
Splines (mathematics)
Tongue
tongue model
Training
Ultrasonic imaging
ultrasound
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Summary:Using data-driven techniques and ultrasound data, it is possible to learn models that reconstruct the tongue shape of a speaker with submillimetric accuracy given the location of 3-4 fleshpoints, and to adapt these models to a new speaker for which little data is available. In practice, tongue contours extracted from ultrasound imaging are often incomplete because of shadowing, noise and other factors. We extend these models to deal with missing data during learning and adaptation, and show that submillimetric accuracy can still be achieved even with relatively large amounts of missing data.
ISBN:1467300454
9781467300452
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2012.6288790