  In this paper, we propose a deep learning-based algorithm to improve the
performance of automatic speech recognition (ASR) systems for aphasia, apraxia,
and dysarthria speech by utilizing electroencephalography (EEG) features
recorded synchronously with aphasia, apraxia, and dysarthria speech. We
demonstrate a significant decoding performance improvement by more than 50\%
during test time for isolated speech recognition task and we also provide
preliminary results indicating performance improvement for the more challenging
continuous speech recognition task by utilizing EEG features. The results
presented in this paper show the first step towards demonstrating the
possibility of utilizing non-invasive neural signals to design a real-time
robust speech prosthetic for stroke survivors recovering from aphasia, apraxia,
and dysarthria. Our aphasia, apraxia, and dysarthria speech-EEG data set will
be released to the public to help further advance this interesting and crucial
research.
