EEG based Continuous Speech Recognition using Transformers

--In this paper we investigate continuous speech recognition using electroencephalography (EEG) features using recently introduced end-to-end transformer based automatic speech recognition (ASR) model. Our results show that transformer based model demonstrate faster inference and training compared to recurrent neural network (RNN) based sequence-to-sequence EEG models but performance of the RNN based models were better than transformer based model during test time on a limited English vocabulary. Continuous speech recognition using non invasive brain signals or electroencephalography (EEG) signals is an emerging area of research where non invasive EEG signals recorded from the scalp of the subject is translated to text. EEG based continuous speech recognition technology enables people with speaking disabilities or people who are not able to speak to have better technology accessibility. Current state-of-the-art voice assistant systems process mainly acoustic input features limiting technology accessibility for people with speaking disabilities or people with no ability to produce voice.