Session-Independent Array-Based EMG-to-Speech Conversion using Convolutional Neural Networksby Lorenz Diener, Gerrit Felsch, Miguel Angrick, Tanja Schultz
Abstract:
This paper presents an evaluation of the performance of EMG-to-Speech conversion based on convolutional neural networks. We present an analysis of two different architectures and network design considerations and evaluate CNN-based systems for their within-session and cross-session performance. We find that they are able to perform on par with feedforward neural networks when trained and evaluated on a single session and outperform them in cross session evaluations.
Reference:
Session-Independent Array-Based EMG-to-Speech Conversion using Convolutional Neural Networks (Lorenz Diener, Gerrit Felsch, Miguel Angrick, Tanja Schultz), In 13th ITG Conference on Speech Communication, 2018.
Bibtex Entry:
@inproceedings{diener2018session,
title={{Session-Independent Array-Based EMG-to-Speech Conversion using Convolutional Neural Networks}},
author={Lorenz Diener and Gerrit Felsch and Miguel Angrick and Tanja Schultz},
booktitle={13th ITG Conference on Speech Communication},
year={2018},
abstract={This paper presents an evaluation of the performance of EMG-to-Speech conversion based on convolutional neural networks. We present an analysis of two different architectures and network design considerations and evaluate CNN-based systems for their within-session and cross-session performance. We find that they are able to perform on par with feedforward neural networks when trained and evaluated on a single session and outperform them in cross session evaluations.},
url={https://www.csl.uni-bremen.de/cms/images/documents/publications/ITG2018_EMG_ConvNets.pdf}
}