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The electroencephalography (EEG) signals recorded in parallel with speech areused to perform isolated and continuous speech recognition. During speakingprocess, one also hears his or her own speech and this speech perception isalso reflected in the recorded EEG signals. In this paper we investigatewhether it is possible to separate out this speech perception component fromEEG signals in order to design more robust EEG based speech recognitionsystems. We further demonstrate predicting EEG signals recorded in parallelwith speaking from EEG signals recorded in parallel with passive listening andvice versa with very low normalized root mean squared error (RMSE). We finallydemonstrate both isolated and continuous speech recognition using EEG signalsrecorded in parallel with listening, speaking and improve the previousconnectionist temporal classification (CTC) model results demonstrated byauthors in [1] using their data set.

Understanding effect of speech perception in EEG based speech recognition systems