Livescu, Karen

Çetin, Ozgur

Hasegawa-Johnson, Mark

King, Simon

Bartels, Chris

Borges, Nash

Kantor, Arthur

Lal, Partha

Yung, Lisa

Bezman, Ari

Dawson-Haggerty, Stephen

Woods, Bronwyn

Frankel, Joe

Magimai-Doss, Mathew

Saenko, Kate

2007-09-18T10:01:47Z

2007-09-18T10:01:47Z

2007

We report on investigations, conducted at the 2006 Johns HopkinsWorkshop, into the use of articulatory features (AFs) for observation and pronunciation models in speech recognition. In the area of observation modeling, we use the outputs of AF classiers both directly, in an extension of hybrid HMM/neural network models, and as part of the observation vector, an extension of the tandem approach. In the area of pronunciation modeling, we investigate a model having multiple streams of AF states with soft synchrony constraints, for both audio-only and audio-visual recognition. The models are implemented as dynamic Bayesian networks, and tested on tasks from the Small-Vocabulary Switchboard (SVitchboard) corpus and the CUAVE audio-visual digits corpus. Finally, we analyze AF classication and forced alignment using a newly collected set of feature-level manual transcriptions.

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K. Livescu, O. Çetin, M. Hasegawa-Johnson, S. King, C. Bartels, N. Borges, A. Kantor, P. Lal, L. Yung, S. Bezman, Dawson-Haggerty, B. Woods, J. Frankel, M. Magimai-Doss, and K. Saenko. Articulatory feature-based methods for acoustic and audio-visual speech recognition: Summary from the 2006 JHU Summer Workshop. In Proc. ICASSP, Honolulu, April 2007.

http://hdl.handle.net/1842/1998

en

speech technology

Articulatory feature-based methods for acoustic and audio-visual speech recognition: Summary from the 2006 JHU Summer Workshop.

Conference Paper