Hidden Markov model classification of myoelectric signals in speech
Hidden Markov model classification of myoelectric signals in speech
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DOI：
10.1109/memb.2002.1044184
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发表时间：
2002-09-01
影响因子：
--
通讯作者：
Lovely, DF
中科院分区：
文献类型：
--
作者：
Chan, ADC;Englehart, K;Lovely, DF
事实证明，使用隐马尔可夫模型 (HMM) 分类器的肌电信号 (MES) 自动语音识别 (ASR) 对时间方差具有弹性，与线性判别分析 (LDA) 分类器相比，其鲁棒性更高。通过优化HMM分类器的特征和结构，提高分类率，可以进一步增强MES ASR的整体性能。尽管如此，HMM 分类器已经表明它可以有效地补充多模态 ASR 系统中的声学分类器。
It has been demonstrated that myoelectric signal (MES) automatic speech recognition (ASR) using an hidden Markov model (HMM) classifier is resilient to temporal variance, which offers improved robustness compared to the linear discriminant analysis (LDA) classifier. The overall performance of the MES ASR can be further enhanced by optimizing the features and structure of the HMM classifier to improve classification rate. Nevertheless, the HMM classifier has already shown that it would effectively complement an acoustic classifier in a multimodal ASR system.