  In this paper we introduce attention-regression model to demonstrate
predicting acoustic features from electroencephalography (EEG) features
recorded in parallel with spoken sentences. First we demonstrate predicting
acoustic features directly from EEG features using our attention model and then
we demonstrate predicting acoustic features from EEG features using a two-step
approach where in the first step we use our attention model to predict
articulatory features from EEG features and then in second step another
attention-regression model is trained to transform the predicted articulatory
features to acoustic features. Our proposed attention-regression model
demonstrates superior performance compared to the regression model introduced
by authors in [1] when tested using their data set for majority of the subjects
during test time. The results presented in this paper further advances the work
described by authors in [1].
