Mathematical foundations of hidden Markov models
Mathematical foundations of hidden Markov models
复制标题
隐马尔可夫模型的数学基础
DOI：
10.1007/978-3-642-83476-9_19
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发表时间：
1988
期刊：
影响因子：
--
通讯作者：
L. Rabiner
中科院分区：
文献类型：
--
作者：
L. Rabiner
信号建模的随机方法变得越来越流行。发生这种情况有两个重要原因。首先，这些模型的数学结构非常丰富，因此可以构成广泛应用的理论基础。其次，如果应用得当，这些模型在实践中可以很好地应用于几个重要的应用。在本文中，我们尝试仔细而系统地回顾一种随机建模的理论方面，即隐马尔可夫模型（HMM），并展示它们如何应用于机器语音识别中的几个问题。
Stochastic methods of signal modeling have become increasingly popular. There are two strong reasons why this has occurred. First the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Second the models, when applied properly, work very well in practice for several important applications. In this paper we attempt to carefully and methodically review the theoretical aspects of one type of stochastic modelling, namely hidden Markov models (HMM’s), and show how they have been applied to a couple of problems in machine recognition of speech.