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.
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