title: Pattern learning with deep neural networks in EMG-based speech recognition.
abstract: We report on classification of phones and phonetic features from facial electromyographic (EMG) data, within the context of our EMG-based Silent Speech interface. In this paper we show that a Deep Neural Network can be used to perform this classification task, yielding a significant improvement over conventional Gaussian Mixture models. Our central contribution is the visualization of patterns which are learned by the neural network. With increasing network depth, these patterns represent more and more intricate electromyographic activity.
authors.forename: Michael
authors.initials: M
authors.lastname: Wand
authors.forename: Tanja
authors.initials: T
authors.lastname: Schultz
mesh_terms: D001185:Artificial Intelligence; D004576:Electromyography / Q000379:methods*; D005145:Face; D005152:Facial Muscles / Q000502:physiology; D006801:Humans; D007802:Language; D016571:Neural Networks, Computer; D033281:Nontherapeutic Human Experimentation; D010363:Pattern Recognition, Automated / Q000379:methods*; D046709:Pattern Recognition, Physiological; D010700:Phonetics; D013060:Speech / Q000502:physiology