Title :
A two-channel training algorithm for hidden Markov model to identify visual speech elements
Author :
Foo, Say Wei ; Lian, Yong ; Dong, Liang
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Abstract :
A novel two-channel algorithm is proposed in this paper for discriminative training of Hidden Markov Models (HMMs). It adjusts the symbol emission coefficients of an existing HMM to maximize the separable distance between a pair of confusable training samples. The method is applied to identify the visemes of visual speech. The results indicate that the two-channel training method provides better accuracy on separating similar visemes than the conventional Baum-Welch estimation.
Keywords :
hidden Markov models; maximum likelihood estimation; speaker recognition; speech recognition; confusable training samples; discriminative training; hidden Markov model; maximum likelihood HMM; separable distance; sequence recognition problems; speaker identification; speech recognition; symbol emission coefficients; two-channel training algorithm; viseme recognition; visemes identification; visual speech elements identification; Convergence; Handwriting recognition; Hidden Markov models; Management training; Maximum likelihood estimation; Signal processing; Speech recognition; Training data;
Conference_Titel :
Circuits and Systems, 2003. ISCAS '03. Proceedings of the 2003 International Symposium on
Print_ISBN :
0-7803-7761-3
DOI :
10.1109/ISCAS.2003.1206038
