标题： 隐马尔可夫模型的调整式维特比训练 显示英文标题

标题： The adjusted Viterbi training for hidden Markov models

摘要： EM算法是隐马尔可夫模型参数估计的主要工具。然而，在应用中，人们通常用Viterbi提取（或训练）代替EM算法，即VT。VT的计算复杂度较低，更稳定，并且更具直观吸引力，但VT估计是有偏的，且不能满足以下固定点性质：假设存在一个无穷大的样本量，并初始化为真实参数，VT通常会偏离初始值。我们提出了调整后的Viterbi训练（VA），这是一种新方法，旨在恢复固定点性质，从而减轻VT估计器的整体不精确性，同时保留基线VT算法的计算优势。在其他模拟研究中已经表明，VA显著提高了混合模型和更一般的HMMs中估计的精度。然而，由于完全是解析的方法，VA校正依赖于无限长的Viterbi对齐和相关的极限概率分布。虽然在混合模型的情况下是显而易见的，但对于更一般的HMMs，这些极限测度的存在并不明显。本文证明了在某些温和条件下，一般HMMs所需的极限分布确实存在。 显示英文摘要

摘要： The EM procedure is a principal tool for parameter estimation in the hidden Markov models. However, applications replace EM by Viterbi extraction, or training (VT). VT is computationally less intensive, more stable and has more of an intuitive appeal, but VT estimation is biased and does not satisfy the following fixed point property. Hypothetically, given an infinitely large sample and initialized to the true parameters, VT will generally move away from the initial values. We propose adjusted Viterbi training (VA), a new method to restore the fixed point property and thus alleviate the overall imprecision of the VT estimators, while preserving the computational advantages of the baseline VT algorithm. Simulations elsewhere have shown that VA appreciably improves the precision of estimation in both the special case of mixture models and more general HMMs. However, being entirely analytic, the VA correction relies on infinite Viterbi alignments and associated limiting probability distributions. While explicit in the mixture case, the existence of these limiting measures is not obvious for more general HMMs. This paper proves that under certain mild conditions, the required limiting distributions for general HMMs do exist.