  The electroencephalography (EEG) signals recorded in parallel with speech are
used to perform isolated and continuous speech recognition. During speaking
process, one also hears his or her own speech and this speech perception is
also reflected in the recorded EEG signals. In this paper we investigate
whether it is possible to separate out this speech perception component from
EEG signals in order to design more robust EEG based speech recognition
systems. We further demonstrate predicting EEG signals recorded in parallel
with speaking from EEG signals recorded in parallel with passive listening and
vice versa with very low normalized root mean squared error (RMSE). We finally
demonstrate both isolated and continuous speech recognition using EEG signals
recorded in parallel with listening, speaking and improve the previous
connectionist temporal classification (CTC) model results demonstrated by
authors in [1] using their data set.
