Lee Jong-Seok, Park Cheol Hoon
IEEE Trans Syst Man Cybern B Cybern. 2010 Aug;40(4):1188-96. doi: 10.1109/TSMCB.2009.2036753. Epub 2010 Jan 8.
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.
我们提出了一种新颖的随机优化算法——混合模拟退火算法（SA），用于训练隐马尔可夫模型（HMM）以进行视觉语音识别。在我们的算法中，SA与一个局部优化算子相结合，该算子用一个更好的解替代当前解，以提高收敛速度和解决方案的质量。我们从数学上证明了算法中目标值序列依概率收敛到全局最优解。该算法被应用于训练用作视觉语音识别器的HMM。虽然HMM常用的训练方法——期望最大化算法，在参数空间中只能达到局部最优，但所提出的方法可以对HMM的参数进行全局优化，从而获得具有更高识别性能的解决方案。通过孤立词识别实验证明了所提出算法相对于传统算法的优越性。
