EEG based Continuous Speech Recognition using Transformers

In this paper we investigate continuous speech recognition using electroencephalography (EEG) features using recently introduced end-to-end transformer based automatic speech recognition (ASR) model. Our results demonstrate that transformer based model demonstrate faster training compared to recurre...

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Bibliographic Details
Published in:arXiv.org 2020-05
Main Authors: Krishna, Gautam, Co Tran, Carnahan, Mason, Tewfik, Ahmed H
Format: Article
Language:English
Subjects:
Alliances
Automatic speech recognition
Electroencephalography
Feature recognition
Model testing
Recurrent neural networks
Transformers
Voice recognition
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Summary:In this paper we investigate continuous speech recognition using electroencephalography (EEG) features using recently introduced end-to-end transformer based automatic speech recognition (ASR) model. Our results demonstrate that transformer based model demonstrate faster training compared to recurrent neural network (RNN) based sequence-to-sequence EEG models and better performance during inference time for smaller test set vocabulary but as we increase the vocabulary size, the performance of the RNN based models were better than transformer based model on a limited English vocabulary.
ISSN:2331-8422