An automatic speech recognition system using neural networks and linear dynamic models to recover and model articulatory traces

6th International Conference on Spoken Language Processing, ICSLP 2000

Volumn , Issue , 2000, Pages

  Frankel, Joe ^a   Richmond, Korin ^a   King, Simon ^a   Taylor, Paul ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMIC MODELS; LINEAR NETWORKS; RECURRENT NEURAL NETWORKS; TELEPHONE SETS;

ARTICULATORY DATA; AUTOMATIC SPEECH RECOGNITION SYSTEM; LINEAR DYNAMIC MODEL; MID-SAGITTAL PLANES; PHONE RECOGNITION; SPEECH RECOGNITION SYSTEMS; SPEECH SIGNALS;

SPEECH RECOGNITION;

EID: 84994254645     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. J. Hogden, A. Lofqvist, V. Gracco, I. Zlokarnik, P. Rubin, and E. Saltzman. Accurate recovery of articulator positions from acoustics: New conclusions based on human data. J. Acoust. Soc. Am., 100(3):1819-1834, September 1996.
2. S. King, A. Wrench. Dynamical system modelling of articulator movement. Proc. ICPhS'99 2259-2263.
3. G. Papcun, J. Hochberg, T. R. Thomas, F. Laroche, J. Zachs, and S. Levy. Inferring articulation and recognising gestures from acoustics with a neural network trained on x-ray microbeam data. J. Acoust. Soc. Am., 92(2):688-700, August 1992.
4. K. Richmond. Estimating velum height from acoustics during continuous speech. Eurospeech 1999, (1): 149-152.
5. M. Ostendorf, V. Digalakis, and O. Kimball. From HMMs to segment models: A unified view of stochastic modeling for speech recognition. IEEE Trans. on Speech and Audio Processing, 4(5), Sept. 1996.