  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
in three participants, 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 audio from neural
recordings, we employ a recurrent encoder-decoder model based on modern deep
learning methods. We find that speech can indeed be reconstructed with
correlations up to 0.8 from these minimally invasive recordings, despite
limited amounts of training data. In particular, the architecture we employ
naturally picks up on the temporal nature of the data and thereby outperforms
an existing benchmark based on non-regressive convolutional neural networks.
