State-of-the-art Speech Recognition using EEG and Towards Decoding of Speech Spectrum From EEG

Abstract

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.

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Cited by in corpus (13)

• Improving EEG based Continuous Speech Recognition
• Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review
• EEG based Continuous Speech Recognition using Transformers
• Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems
• Speech Recognition With No Speech Or With Noisy Speech Beyond English
• Improving EEG based continuous speech recognition using GAN
• Voice Activity Detection in presence of background noise using EEG
• Speaker Identification using EEG
• Continuous Silent Speech Recognition using EEG
• Continuous Speech Recognition using EEG and Video
• Understanding effect of speech perception in EEG based speech recognition systems
• Predicting Different Acoustic Features from EEG and towards direct synthesis of Audio Waveform from EEG
• Spoken Speech Enhancement using EEG