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. Efforts to reconstruct speech from brain activity have shown their potential using invasive measures of spoken speech data, but 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, an automatic speech recognition decoder contributed to decomposing the phonemes of the generated speech, demonstrating 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.
|Title of host publication
|AAAI-23 Technical Tracks 5
|Brian Williams, Yiling Chen, Jennifer Neville
|Number of pages
|Published - 2023 Jun 27
|37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States
Duration: 2023 Feb 7 → 2023 Feb 14
|Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
|37th AAAI Conference on Artificial Intelligence, AAAI 2023
|23/2/7 → 23/2/14
Bibliographical noteFunding Information:
This work was supported by Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2021-0-02068, Artificial Intelligence Innovation Hub; No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning; No. 2019-0-00079, Artificial Intelligence Graduate School Program(Korea University)).
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ASJC Scopus subject areas
- Artificial Intelligence