基于模糊c均值和模糊熵的二阶隐马尔可夫模型

2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Pub Date : 2010-12-06 DOI:10.1109/ICICISYS.2010.5658727

D. Shiping, Wang Jian, Wei Yuming

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引用次数: 1

摘要

二阶隐马尔可夫模型(HMM2)在模式识别特别是语音识别中得到了广泛的应用。它们的主要优点是能够模拟可变长度的噪声时间信号。本文提出了一种基于模糊c均值(FCM)和模糊熵(FE)的HMM2扩展，称为FCM-FE-HMM2。通过建立广义模糊目标函数，从理论上推导出求解FCM-FE-HMM2模型训练问题的几个新公式。

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Second-order hidden Markov models based on the fuzzy c-means and fuzzy entropy

Second-order hidden Markov models (HMM2) have been widely used in pattern recognition, especially in speech recognition. Their main advantages are their capabilities to model noisy temporal signals of variable length. This paper presents an extension of HMM2 based on the fuzzy c-means (FCM) and fuzzy entropy (FE) referred to as FCM-FE-HMM2. By building up a generalised fuzzy objective function, several new formulae solving model training problem are theoretically derived for FCM-FE-HMM2.

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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems

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