Improving EEG based continuous speech recognition using GAN
arXiv:2006.01260
Abstract
In this paper we demonstrate that it is possible to generate more meaningful electroencephalography (EEG) features from raw EEG features using generative adversarial networks (GAN) to improve the performance of EEG based continuous speech recognition systems. We improve the results demonstrated by authors in [1] using their data sets for for some of the test time experiments and for other cases our results were comparable with theirs. Our proposed approach can be implemented without using any additional sensor information, whereas in [1] authors used additional features like acoustic or articulatory information to improve the performance of EEG based continuous speech recognition systems.
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References in corpus (6)
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- Empirical Evaluation of Rectified Activations in Convolutional Network
- Attention-Based Models for Speech Recognition
- State-of-the-art Speech Recognition using EEG and Towards Decoding of Speech Spectrum From EEG
- Improving EEG based Continuous Speech Recognition