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EEG based Continuous Speech Recognition using Transformers

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arxiv 2001.00501 v3 pith:354FLCEJ submitted 2019-12-31 eess.AS cs.LGcs.SDstat.ML

Gautam Krishna , Co Tran , Mason Carnahan , Ahmed H Tewfik This is my paper
classification eess.AScs.LGcs.SDstat.ML
keywords modelrecognitionspeechtransformervocabularybettercontinuousdemonstrate
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

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  1. Transformer-based EEG Decoding: A Survey

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A survey that classifies Transformer-based EEG decoding models into backbone, hybrid, and customized categories and reviews their applications and limitations.

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