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In this paper, we propose a deep learning-based algorithm to improve theperformance of automatic speech recognition (ASR) systems for aphasia, apraxia,and dysarthria speech by utilizing electroencephalography (EEG) featuresrecorded synchronously with aphasia, apraxia, and dysarthria speech. Wedemonstrate a significant decoding performance improvement by more than 50\%during test time for isolated speech recognition task and we also providepreliminary results indicating performance improvement for the more challengingcontinuous speech recognition task by utilizing EEG features. The resultspresented in this paper show the first step towards demonstrating thepossibility of utilizing non-invasive neural signals to design a real-timerobust speech prosthetic for stroke survivors recovering from aphasia, apraxia,and dysarthria. Our aphasia, apraxia, and dysarthria speech-EEG data set willbe released to the public to help further advance this interesting and crucialresearch.

Brain Signals to Rescue Aphasia, Apraxia and Dysarthria Speech Recognition