  Translating imagined speech from human brain activity into voice is a
challenging and absorbing research issue that can provide new means of human
communication via brain signals. Endeavors toward reconstructing speech from
brain activity have shown their potential using invasive measures of spoken
speech data, however, have faced challenges in reconstructing imagined speech.
In this paper, we propose NeuroTalk, which converts non-invasive brain signals
of imagined speech into the user's own voice. Our model was trained with spoken
speech EEG which was generalized to adapt to the domain of imagined speech,
thus allowing natural correspondence between the imagined speech and the voice
as a ground truth. In our framework, automatic speech recognition decoder
contributed to decomposing the phonemes of generated speech, thereby displaying
the potential of voice reconstruction from unseen words. Our results imply the
potential of speech synthesis from human EEG signals, not only from spoken
speech but also from the brain signals of imagined speech.
