Abstract
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.
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