title: Syllable-based speech recognition using EMG.
abstract: 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).
authors.affiliation: Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, Spain. edulop@unizar.es
authors.forename: Eduardo
authors.initials: E
authors.lastname: Lopez-Larraz
authors.forename: Oscar M
authors.initials: OM
authors.lastname: Mozos
authors.forename: Javier M
authors.initials: JM
authors.lastname: Antelis
authors.forename: Javier
authors.initials: J
authors.lastname: Minguez
mesh_terms: D004576:Electromyography / Q000379:methods*; D005152:Facial Muscles / Q000502:physiology*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D012660:Semantics; D013060:Speech / Q000502:physiology*; D013068:Speech Production Measurement / Q000379:methods*; D049250:Speech Recognition Software