--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.

--In this paper we investigate whether electroen-cephalography (EEG) features can be used to improve the performance of continuous visual speech recognition systems. We implemented a connectionist temporal classification (CTC) based end-to-end automatic speech recognition (ASR) model for performing recognition. Our results demonstrate that EEG features are helpful in enhancing the performance of continuous visual speech recognition systems. In recent years there has been lot of interesting work done in the fields of lip reading and audio visual speech recognition. In [1] authors demonstrated end-to-end sentence level lip reading and in [2] authors demonstrated deep learning based end-to- end audio visual speech recognition.

Improving EEG based Continuous Speech Recognition Gautam Krishna Brain Machine Interface Lab The University of T exas at Austin Austin, Texas Co Tran Brain Machine Interface Lab The University of T exas at Austin Austin, Texas Mason Carnahan Brain Machine Interface Lab The University of T exas at Austin Austin, Texas Y an Han Brain Machine Interface Lab The University of T exas at Austin Austin, Texas Ahmed H Tewfik Brain Machine Interface Lab The University of T exas at Austin Austin, Texas Abstract --In this paper we introduce various techniques to improve the performance of electroencephalography (EEG) features based continuous speech recognition (CSR) systems. A connectionist temporal classification (CTC) based automatic speech recognition (ASR) system was implemented for performing recognition. We introduce techniques to initialize the weights of the recurrent layers in the encoder of the CTC model with more meaningful weights rather than with random weights and we make use of an external language model to improve the beam search during decoding time. We finally study the problem of predicting articulatory features from EEG features in this paper . ASR systems forms front end or back end in many state of the art voice assistant systems like Bixby, Alexa,Siri,Cortana etc.