26 citations · 28 across the 11 of their papers we have counts for
6 papers · 1 filter
Neural Speech Embeddings for Speech Synthesis Based on Deep Generative Networks
Seo-Hyun Lee, Young-Eun Lee, Soowon Kim +3
Brain-to-speech technology represents a fusion of interdisciplinary applications encompassing fields of artificial intelligence, brain-computer interfaces, and speech synthesis. Ne…
Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals
Young-Eun Lee, Seo-Hyun Lee, Soowon Kim +3
Recent advances in brain-computer interface (BCI) technology, particularly based on generative adversarial networks (GAN), have shown great promise for improving decoding performan…
Brain-Driven Representation Learning Based on Diffusion Model
Soowon Kim, Seo-Hyun Lee, Young-Eun Lee +3
Interpreting EEG signals linked to spoken language presents a complex challenge, given the data's intricate temporal and spatial attributes, as well as the various noise factors. D…
Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG
Soowon Kim, Young-Eun Lee, Seo-Hyun Lee +1
Decoding EEG signals for imagined speech is a challenging task due to the high-dimensional nature of the data and low signal-to-noise ratio. In recent years, denoising diffusion pr…
Subject-Independent Classification of Brain Signals using Skip Connections
Soowon Kim, Ji-Won Lee, Young-Eun Lee +1
Untapped potential for new forms of human-to-human communication can be found in the active research field of studies on the decoding of brain signals of human speech. A brain-comp…
Towards Voice Reconstruction from EEG during Imagined Speech
Young-Eun Lee, Seo-Hyun Lee, Sang-Ho Kim +1
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.…