# EEG based Continuous Speech Recognition using Transformers

arXiv: https://arxiv.org/abs/2001.00501 · HTML: https://mlsanity.com/paper/2001.00501/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2203.16336/) (2022)
- [Efficacy of Transformer Networks for Classification of Raw EEG Data](https://mlsanity.com/paper/2202.05170/) (2022)
- [TEMGNet: Deep Transformer-based Decoding of Upperlimb sEMG for Hand Gestures Recognition](https://mlsanity.com/paper/2109.12379/) (2021)
- [Transformer-based EEG Decoding: A Survey](https://mlsanity.com/paper/2507.02320/) (2025)
- [EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers](https://mlsanity.com/paper/2209.11172/) (2022)
- [Lightweight Convolution Transformer for Cross-patient Seizure Detection in Multi-channel EEG Signals](https://mlsanity.com/paper/2305.04325/) (2023)
- [EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification](https://mlsanity.com/paper/2107.03983/) (2021)

## Abstract (arXiv)

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.