Hybrid Simulated Annealing and Its Application to Optimization of Hidden Markov Models for Visual Speech Recognition
Hybrid Simulated Annealing and Its Application to Optimization of Hidden Markov Models for Visual Speech Recognition
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DOI：
10.1109/tsmcb.2009.2036753
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发表时间：
2010-08
期刊：
影响因子：
--
通讯作者：
Jong-Seok Lee;C. Park
中科院分区：
文献类型：
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作者：
Jong-Seok Lee;C. Park
本文提出了一种新的随机优化算法--混合模拟退火算法（SA），用于训练隐马尔可夫模型（HMRM）用于视觉语音识别。在我们的算法中，SA是结合了一个局部优化算子，取代当前的一个更好的解决方案，以提高收敛速度和解决方案的质量。我们从数学上证明了目标值序列依概率收敛于算法的全局最优解。该算法被应用于训练Hestive，用作视觉语音识别器。虽然流行的训练方法的HSPINESS，期望最大化算法，只能达到局部最优的参数空间，所提出的方法可以执行全局优化的HSPINESS的参数，从而获得解决方案，产生更好的识别性能。通过孤立词识别实验证明了该算法相对于传统算法的优越性。
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.