Towards Voice Reconstruction from EEG during Imagined Speech
DOI:
https://doi.org/10.1609/aaai.v37i5.25745Keywords:
HAI: Brain-Sensing and Analysis, CMS: Brain Modeling, HAI: Communication ProtocolsAbstract
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.Downloads
Published
2023-06-26
How to Cite
Lee, Y.-E., Lee, S.-H., Kim, S.-H., & Lee, S.-W. (2023). Towards Voice Reconstruction from EEG during Imagined Speech. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 6030-6038. https://doi.org/10.1609/aaai.v37i5.25745
Issue
Section
AAAI Technical Track on Humans and AI