Toward Fully-End-to-End Listened Speech Decoding from EEG Signals
This work addresses speech decoding from brain activity, which could aid in communication for individuals with speech impairments, but it appears incremental as it builds on existing EEG-based methods.
The authors tackled the problem of directly reconstructing listened speech waveforms from EEG signals without intermediate acoustic feature processing, achieving superior performance on objective metrics compared to prior works.
Speech decoding from EEG signals is a challenging task, where brain activity is modeled to estimate salient characteristics of acoustic stimuli. We propose FESDE, a novel framework for Fully-End-to-end Speech Decoding from EEG signals. Our approach aims to directly reconstruct listened speech waveforms given EEG signals, where no intermediate acoustic feature processing step is required. The proposed method consists of an EEG module and a speech module along with a connector. The EEG module learns to better represent EEG signals, while the speech module generates speech waveforms from model representations. The connector learns to bridge the distributions of the latent spaces of EEG and speech. The proposed framework is both simple and efficient, by allowing single-step inference, and outperforms prior works on objective metrics. A fine-grained phoneme analysis is conducted to unveil model characteristics of speech decoding. The source code is available here: github.com/lee-jhwn/fesde.