Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework

• URL: http://arxiv.org/abs/2111.01457v1
• Date: Tue, 2 Nov 2021 09:43:21 GMT
• Title: Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework
• Authors: Jonas Kohler, Maarten C. Ottenhoff, Sophocles Goulis, Miguel Angrick, Albert J. Colon, Louis Wagner, Simon Tousseyn, Pieter L. Kubben, Christian Herff
• Abstract summary: Speech Neuroprostheses have the potential to enable communication for people with dysarthria or anarthria. Recent advances have demonstrated high-quality text decoding and speech synthesis from electrocorticographic grids placed on the cortical surface.
• Score: 1.623136488969658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech Neuroprostheses have the potential to enable communication for people with dysarthria or anarthria. Recent advances have demonstrated high-quality text decoding and speech synthesis from electrocorticographic grids placed on the cortical surface. Here, we investigate a less invasive measurement modality, namely stereotactic EEG (sEEG) that provides sparse sampling from multiple brain regions, including subcortical regions. To evaluate whether sEEG can also be used to synthesize high-quality audio from neural recordings, we employ a recurrent encoder-decoder framework based on modern deep learning methods. We demonstrate that high-quality speech can be reconstructed from these minimally invasive recordings, despite a limited amount of training data. Finally, we utilize variational feature dropout to successfully identify the most informative electrode contacts.

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