Improving EEG based Continuous Speech Recognition

Abstract

In this paper we introduce various techniques to improve the performance of electroencephalography (EEG) features based continuous speech recognition (CSR) systems. A connectionist temporal classification (CTC) based automatic speech recognition (ASR) system was implemented for performing recognition. We introduce techniques to initialize the weights of the recurrent layers in the encoder of the CTC model with more meaningful weights rather than with random weights and we make use of an external language model to improve the beam search during decoding time. We finally study the problem of predicting articulatory features from EEG features in this paper.

On preparation for submission to EUSIPCO 2020. arXiv admin note: text overlap with arXiv:1911.04261, arXiv:1906.08871

References in corpus (4)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• Attention-Based Models for Speech Recognition
• State-of-the-art Speech Recognition using EEG and Towards Decoding of Speech Spectrum From EEG

Cited by in corpus (7)

• EEG based Continuous Speech Recognition using Transformers
• Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems
• Improving EEG based continuous speech recognition using GAN
• Speech Recognition using EEG signals recorded using dry electrodes
• Understanding effect of speech perception in EEG based speech recognition systems
• Continuous Silent Speech Recognition using EEG
• Predicting Different Acoustic Features from EEG and towards direct synthesis of Audio Waveform from EEG