Advanced SearchSearch Tips
Monophthong Recognition Optimizing Muscle Mixing Based on Facial Surface EMG Signals
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Monophthong Recognition Optimizing Muscle Mixing Based on Facial Surface EMG Signals
Lee, Byeong-Hyeon; Ryu, Jae-Hwan; Lee, Mi-Ran; Kim, Deok-Hwan;
  PDF(new window)
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.
EMG signal;speech recognition;facial muscles;feature extraction;classifier;
 Cited by
무성인식을 위한 조음위치에 따른 물리센서 기반 한국어 자음 분류,신진호;류재환;이병현;김덕환;

한국차세대컴퓨팅학회논문지, 2016. vol.12. 5, pp.15-22
Apple Siri,

Google Voice Actions,

S. Kumar, D. K. Kumar, M. Alemu, M. Berry, "EMG Based Voice Recognition", in Proc. of IEEE Conf. on Intelligent Sensors, Sensor Network and Information Processing, pp. 593-597, Melbourne, Australia, Dec 2004.

J. F. Gemmeke, T. Virtanen, A. Hurmalainen, "Exemplar-Based Sparse Representations for Noise Robust Automatic Speech Recognition", IEEE Trans. Audio, Speech and Language Processing, Vol. 19, no. 7, Sep. 2011.

T. Heistermann, M. Janke, M. Wand, T. Schultz, "Spatial Artifact Detection for Multi-Channel EMG-Based Speech Recognition", Internatinal Conf. on Bio-inspired Systems and Signal Processing, pp. 189-196, Angers, France, Mar 2014.

H. Manabe, Z. Zhang, "Multi-stream HMM for EMG-based speech recognition", in Proc. of IEEE Conf. on Engineering in Medicine and Biology Society, pp. 4389-4392, San Francisco, CA, Jun 2004.

Y. Deng, R. Patel, J. T. Heaton, G. Colby, L. D. Gilmore, J. Cabrera, S. H. Roy, C. J. D. Luca, G. S. Meltzner, "Disordered speech recognition using acoustic and sEMG signals", INTERSPEECH 2009, pp. 644-647, Brighton, UK, Sep 2009.

C. Jorgensen, S. Dusan, "Speech interfaces based upon surface electromyography", Speech Communication, Vol. 20, no. 4, pp. 354-366, Apr 2010.

A.D.C. Chan, K. Englehart, B, Hudgins, D.F. Lovely, "Hidden Markov Model Classification of Myoelectric Signals in Speech", IEEE Trans. Engineering in Medicine and Biology Magazine, Vol. 21, no. 4, pp. 143-146, Sep 2002. crossref(new window)

H. Yong, "A Typological Study on Korean Vowel Systems", Language and Linguistics, pp. 175-200, Vol. 61, Nov 2013.

A. Phinyomark, S. Hirunviriya, C. Limsakul, P. Phukpattaranont, "Evaluation of EMG Feature Extraction for Hand Movement Recognition Based on Euclidean Distance and Standard Deviation", in Proc. of IEEE Conf. on ECTI, pp.856-860, Chiang Mai, Thailand, May 2010.

E. Scheme, K. Englehart, "On the Robustness of EMG Features for Pattern Recognition Based Myoelectric Control; A Multi-Dataset Comparison", in Proc. of IEEE Conf. on EMBS, pp.650-653, Chicago, USA, Aug 2014.

B-H. Lee, J-H. Ryu, M-R. Lee, S-H. Kim, M. Z. Uddin, D-H. Kim, "Monophthong recognition using feature and muscle selection based on facial surface EMG signals", in Proc. of The IEEK Conf. on Summer Conference, pp. 933-936, Jeju, Korea, Jun 2015.

N. Srisuwan, P. phukpattaranont, C. Limsakul, "Three Steps of Neuron Network Classification for EMG-based Thai Tones Speech Recognition", in Proc. of IEEE Conf. on ECTI, pp. 1-6, Krabi, Thailand, May 2013.

E. Lopez-Larraz, O. M. Mozos, J. M. Antelis, J. Minguez, "Syllable-Based Speech Recognition Using EMG", in Proc. of IEEE Conf. on EMBS, pp. 4699-4702, Buenos Aires, Argentina, Aug 2010.