Improving EEG based continuous speech recognition using GAN
This work addresses EEG-based speech recognition, which could aid communication for individuals with speech impairments, but it appears incremental as it builds on existing datasets and methods.
The authors tackled the problem of EEG-based continuous speech recognition by generating more meaningful EEG features using GANs, improving or matching prior results without needing additional sensor information.
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.