调整Viterbi训练
Adjusted Viterbi training
摘要：
我们研究修改的维特比训练（VT）算法，以估计一般隐马尔可夫模型（HMM）中的排放参数，特别是在混合模型。受应用程序的VT HMM用于语音识别，自然语言建模，图像分析和生物信息学的启发，我们调查的可能性，减轻VT的不一致性，同时控制额外的计算量。具体来说，我们建议使VT渐近固定的EM算法的参数的真实值。这依赖于无限维特比比对和与之相关联的限制性概率分布。然而，本文的重点是混合模型，HMM的一个重要的情况下，其中的极限分布总是可以精确计算，找到这样的极限分布一般HMM提出了一个更具挑战性的任务，根据我们正在进行的调查。一个单变量高斯混合的模拟表明，我们的中心算法（VA 1）可以显着提高精度，而不会在计算时间上花费太多。我们还提出了VA 2，一个更先进的数学校正VT，通过仿真验证其快速收敛和高精度，其计算的可行性仍有待于在未来的工作中进行研究。
英文摘要：
We study modifications of the Viterbi Training (VT) algorithm to estimate emission parameters in Hidden Markov Models (HMM) in general, and in mixure models in particular. Motivated by applications of VT to HMM that are used in speech recognition, natural language modeling, image analysis, and bioinformatics, we investigate a possibility of alleviating the inconsistency of VT while controlling the amount of extra computations. Specifically, we propose to enable VT to asymptotically fix the true values of the parameters as does the EM algorithm. This relies on infinite Viterbi alignment and an associated with it limiting probability distribution. This paper, however, focuses on mixture models, an important case of HMM, wherein the limiting distribution can always be computed exactly; finding such limiting distribution for general HMM presents a more challenging task under our ongoing investigation. A simulation of a univariate Gaussian mixture shows that our central algorithm (VA1) can dramatically improve accuracy without much cost in computation time. We also present VA2, a more mathematically advanced correction to VT, verify by simulation its fast convergence and high accuracy; its computational feasibility remains to be investigated in future work.
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