Fundamentals of speech recognition
Fundamentals of speech recognition
Convergence of the simulated annealing algorithm for continuous global optimization
Journal of Optimization Theory and Applications
The M2VTS Multimodal Face Database (Release 1.00)
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Proceedings of the 6th international conference on Multimodal interfaces
An evaluation of visual speech features for the tasks of speech and speaker recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
A review of speech-based bimodal recognition
IEEE Transactions on Multimedia
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We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization. operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.