Speech recognition using dynamical model of speech production

The model consists of an articulator and its control command sequences. The latter has linguistic information of speech and the former has the articulatory information which determines the transformation from linguistic intentions to speech signals. This separation allows the speech recognition model to be more controllable. It also provides new approaches to coarticulation modeling. The effectiveness of the proposed model was examined by speaker-independent letter recognition experiments.<<ETX>>

[1]  Esther Levin,et al.  Word recognition using hidden control neural architecture , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[2]  H. Bourlard,et al.  Links Between Markov Models and Multilayer Perceptrons , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Naftali Tishby,et al.  A dynamical systems approach to speech processing , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[4]  Biing-Hwang Juang,et al.  New discriminative training algorithms based on the generalized probabilistic descent method , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.

[5]  Sadaoki Furui,et al.  Speaker-independent isolated word recognition using dynamic features of speech spectrum , 1986, IEEE Trans. Acoust. Speech Signal Process..

[6]  Ken-ichi Iso,et al.  Speaker-independent word recognition using a neural prediction model , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[7]  Alex Waibel,et al.  Large vocabulary recognition using linked predictive neural networks , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[8]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[9]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[10]  Hsiao-Wuen Hon,et al.  An overview of the SPHINX speech recognition system , 1990, IEEE Trans. Acoust. Speech Signal Process..

[11]  Eric Vatikiotis-Bateson,et al.  Forward Dynamics Modeling of Speech Motor Control Using Physiological Data , 1991, NIPS.