Voice rehabilitation is needed after several diseases, when a subject’s vocal ability is compromised by surgical interference or removal of phonation organs (e.g. the larynx), by neural degeneration or by neurological injury to the motor component of the motor-speech system in the phonation area of the brain (e.g. dysarthria in Parkinson disease). A novel approach to voice rehabilitation consists of predicting the phonetic control sequence of the voice-production ap-paratus (larynx, tongue, etc.) by drawing inferences on the basis of myoelectric (EMG) signals captured by a set of contact electrodes, applied to the neck area of a subject with important phonatory alteration (e.g. laryngectomised) and intact neural control. The inference paradigm is based on an EFuNN (Evolving Fuzzy Neural Network) that has been trained to use the sampled EMG signal to predict the phoneme that corresponds to the motor control of the sublingual muscle movements monitored at phonation time. A phoneme-to-speech synthesizer generates audio output corresponding to the utterance the subject has tried to enunciate.

Myo-To-Speech - Evolving Fuzzy-Neural Network Prediction of Speech Utterances from Myoelectric Signals / M. Malcangi, G. Felisati, A. Saibene, E. Alfonsi, M. Fresia, R. Maffioletti, H. Quan (COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE). - In: Engineering Applications of Neural Networks / [a cura di] E. Pimenidis, C.Jayne. - Prima edizione. - [s.l] : Springer Nature, 2018. - ISBN 9783319982038. - pp. 158-168 (( Intervento presentato al 19. convegno Engineering Applications of Neural Networks tenutosi a Bristol nel 2018 [10.1007/978-3-319-98204-5_13].

Myo-To-Speech - Evolving Fuzzy-Neural Network Prediction of Speech Utterances from Myoelectric Signals

M. Malcangi
Investigation
;
G. Felisati
Membro del Collaboration Group
;
A. Saibene
Membro del Collaboration Group
;
2018

Abstract

Voice rehabilitation is needed after several diseases, when a subject’s vocal ability is compromised by surgical interference or removal of phonation organs (e.g. the larynx), by neural degeneration or by neurological injury to the motor component of the motor-speech system in the phonation area of the brain (e.g. dysarthria in Parkinson disease). A novel approach to voice rehabilitation consists of predicting the phonetic control sequence of the voice-production ap-paratus (larynx, tongue, etc.) by drawing inferences on the basis of myoelectric (EMG) signals captured by a set of contact electrodes, applied to the neck area of a subject with important phonatory alteration (e.g. laryngectomised) and intact neural control. The inference paradigm is based on an EFuNN (Evolving Fuzzy Neural Network) that has been trained to use the sampled EMG signal to predict the phoneme that corresponds to the motor control of the sublingual muscle movements monitored at phonation time. A phoneme-to-speech synthesizer generates audio output corresponding to the utterance the subject has tried to enunciate.
EFuNN; Evolving Fuzzy Neural Network; Voice Dysarthria; Voice Rehabilitation; Myoelectric Signal
Settore INF/01 - Informatica
2018
UWE -University West England, INNS - International Neural Networks Society - INNS
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/605116
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