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http://dx.doi.org/10.5573/ieie.2016.53.3.143

Monophthong Recognition Optimizing Muscle Mixing Based on Facial Surface EMG Signals  

Lee, Byeong-Hyeon (Department of Electronic Engineering, Inha University)
Ryu, Jae-Hwan (Department of Electronic Engineering, Inha University)
Lee, Mi-Ran (Department of Electronic Engineering, Inha University)
Kim, Deok-Hwan (Department of Electronic Engineering, Inha University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.53, no.3, 2016 , pp. 143-150 More about this Journal
Abstract
In this paper, we propose Korean monophthong recognition method optimizing muscle mixing based on facial surface EMG signals. We observed that EMG signal patterns and muscle activity may vary according to Korean monophthong pronunciation. We use RMS, VAR, MMAV1, MMAV2 which were shown high recognition accuracy in previous study and Cepstral Coefficients as feature extraction algorithm. And we classify Korean monophthong by QDA(Quadratic Discriminant Analysis) and HMM(Hidden Markov Model). Muscle mixing optimized using input data in training phase, optimized result is applied in recognition phase. Then New data are input, finally Korean monophthong are recognized. Experimental results show that the average recognition accuracy is 85.7% in QDA, 75.1% in HMM.
Keywords
EMG signal; speech recognition; facial muscles; feature extraction; classifier;
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