Syllable-based speech recognition using EMG

This paper presents a silent-speech interface based on electromyographic (EMG) signals recorded in the facial muscles. The distinctive feature of this system is that it is based on the recognition of syllables instead of phonemes or words, which is a compromise between both approaches with advantages as (a) clear delimitation and identification inside a word, and (b) reduced set of classification groups. This system transforms the EMG signals into robust-in-time feature vectors and uses them to train a boosting classifier. Experimental results demonstrated the effectiveness of our approach in three subjects, providing a mean classification rate of almost 70% (among 30 syllables).

[1]  Quan Zhou,et al.  Improved Phoneme-Based Myoelectric Speech Recognition , 2009, IEEE Transactions on Biomedical Engineering.

[2]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[3]  H. Manabe,et al.  Multi-stream HMM for EMG-based speech recognition , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Cheng-Ning Huang,et al.  The Review of Applications and Measurements in Facial Electromyography , 2004 .

[5]  D.K. Kumar,et al.  Unspoken Vowel Recognition Using Facial Electromyogram , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[7]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[8]  A.K. Barros,et al.  Subvocal Speech Recognition Based on EMG Signal Using Independent Component Analysis and Neural Network MLP , 2008, 2008 Congress on Image and Signal Processing.

[9]  Kim Binsted,et al.  Small-vocabulary speech recognition using surface electromyography , 2006, Interact. Comput..

[10]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[11]  A. J. Fridlund,et al.  Guidelines for human electromyographic research. , 1986, Psychophysiology.

[12]  Betty Tuller,et al.  Electromyographic study of the jaw muscles during speech , 1981 .

[13]  Sridhar P. Arjunan,et al.  Vowel recognition of English and German language using facial movement (SEMG) for speech control based HCI , 2006 .

[14]  Antonio Ríos Mestre.  La transcripción fonética automática del Diccionario Electrónico de Formas Simples Flexivas del Español: un estudio fonológico en el léxico. , 1999 .

[15]  B. Hudgins,et al.  Hidden Markov model classification of myoelectric signals in speech , 2001, IEEE Engineering in Medicine and Biology Magazine.

[16]  Ian Witten,et al.  Data Mining , 2000 .

[17]  Chuck,et al.  Sub Auditory Speech Recognition based on EMG/EPG Signals , 2022 .