State-of-the-art Speech Recognition using EEG and Towards Decoding of Speech Spectrum From EEG
This work addresses speech recognition and synthesis from EEG signals, which could aid in communication for individuals with speech impairments, but it appears incremental as it builds on existing methods.
The paper tackled continuous noisy speech recognition using EEG signals on English vocabulary with state-of-the-art ASR models and demonstrated decoding of speech spectrum from EEG using LSTM and GAN-based models, achieving results that show feasibility under different experimental conditions.
In this paper we first demonstrate continuous noisy speech recognition using electroencephalography (EEG) signals on English vocabulary using different types of state of the art end-to-end automatic speech recognition (ASR) models, we further provide results obtained using EEG data recorded under different experimental conditions. We finally demonstrate decoding of speech spectrum from EEG signals using a long short term memory (LSTM) based regression model and Generative Adversarial Network (GAN) based model. Our results demonstrate the feasibility of using EEG signals for continuous noisy speech recognition under different experimental conditions and we provide preliminary results for synthesis of speech from EEG features.