HEALTHCARE AUDIO EVENT CLASSIFICATION USING HIDDEN MARKOV MODELS AND HIERARCHICAL HIDDEN MARKOV MODELS

被引：0

作者：

Peng, Ya-Ti ^{[1
]}

Lin, Ching-Yung ^{[2
]}

Sun, Ming-Ting ^{[1
]}

Tsai, Kun-Cheng ^{[3
]}

机构：

[1] Univ Washington, Dept Elect Engn, Seattle, WA 98195 USA

[2] IBM Corp, TJ Watson Res Ctr, Hawthorne, NY 10532 USA

[3] Inst Informat Ind, Taipei, Taiwan

来源：

ICME: 2009 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-3 | 2009年

关键词：

Audio; HMMs; HHMMs; Healthcare; SPEECH RECOGNITION; SEGMENTATION;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Audio is a useful modality complement to video for healthcare monitoring. In this paper, we investigate the use of Hierarchical Hidden Markov Models (HHMMs) for healthcare audio event classification. We show that HHIMM can handle audio events with recursive patterns to improve the classification performance. We also propose a model fusion method to cover large variations often existing in healthcare audio events. Experimental results from classifying key eldercare audio events show the effectiveness of the model fusion method for healthcare audio event classification.

引用

页码：1218 / +

页数：2

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