Smyth P, Heckerman D, Jordan M I
Department of Information and Computer Science, University of California at Irvine 92697-3425, USA.
Neural Comput. 1997 Feb 15;9(2):227-69. doi: 10.1162/neco.1997.9.2.227.
Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas, including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper presents a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach.
用于对随机变量的依赖性进行建模的图形技术已经在包括统计学、统计物理学、人工智能、语音识别、图像处理和遗传学等各种不同领域中得到了探索。在这些研究领域中，用于处理这些模型的形式体系相对独立地得到了发展。在本文中，我们在概率独立性网络（PIN）的通用框架内探索隐马尔可夫模型（HMM）及相关结构。本文对PIN的基本原理进行了自成体系的综述。结果表明，用于HMM的著名的前向-后向（F-B）算法和维特比算法是用于任意PIN的更通用推理算法的特殊情况。此外，针对更通用图形模型的推理和估计算法的存在为希望探索更丰富的HMM结构类别的HMM从业者提供了一组分析工具。引入了用于处理语音识别中的传感器融合和协同发音的相对复杂模型的示例，并在图形模型框架内进行了处理，以说明通用方法的优点。
