  In this paper we demonstrate speech synthesis using different
electroencephalography (EEG) feature sets recently introduced in [1]. We make
use of a recurrent neural network (RNN) regression model to predict acoustic
features directly from EEG features. We demonstrate our results using EEG
features recorded in parallel with spoken speech as well as using EEG recorded
in parallel with listening utterances. We provide EEG based speech synthesis
results for four subjects in this paper and our results demonstrate the
feasibility of synthesizing speech directly from EEG features.
