Myo-To-Speech - Evolving Fuzzy-Neural Network Prediction of Speech Utterances from Myoelectric Signals
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 apparatus (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.
KeywordsEFuNN Evolving Fuzzy Neural Network Voice dysarthria Voice rehabilitation Myoelectric signal
A special acknowledgment is due to Prof. Nikola Kasabov, Auckland University of Technology, Director KEDRI – Knowledge Engineering and Discovery Research Institute, for his invaluable suggestions on how to get the most from the EFuNN’s evolving capabilities.
Acknowledgment is also due to Jan Hein Broeders (Analog Devices’ healthcare business-development manager for EMEA) for his precious support and expertise in hardware prototyping, especially for the analog front-end (AFE) subsystem.
- 1.Balasubramanian, T.: Voice rehabilitation following total laryngectomy. Otoryngology Online J. 5(1), 5 (2015)Google Scholar
- 10.Mac Neilage, P.F.: The frame/content theory of evolution of speech production. Behav. Brain Sci. 21, 499–511 (1998)Google Scholar
- 17.Kasabov, N.: EFuNN, IEEE Tr SMC (2001)Google Scholar
- 18.Kasabov, N.: Evolving fuzzy neural networks – algorithms, applications and biological motivation. In: Yamakawa, T., Matsumoto, G. (eds.) Methodologies for the Conception, Design and Application of the Soft Computing, pp. 271–274. World Computing (1998)Google Scholar
- 20.Zahner, M., Janke, M., Wand, M., Schultz, T.: Conversion from facial myoelectric signals to speech: a unit selection approach. In: Interspeech, pp. 1184–1188 (2014)Google Scholar