论文标题
使用GAN改善基于脑电图的连续语音识别
Improving EEG based continuous speech recognition using GAN
论文作者
论文摘要
在本文中，我们证明，使用生成的对抗网络（GAN），可以从RAW EEG功能（GAN）中生成更有意义的脑电图（EEG）功能，以提高基于EEG的连续语音识别系统的性能。我们使用对某些测试时间实验的数据集进行了[1]中作者所证明的结果，而在其他情况下，我们的结果与他们的结果相当。我们提出的方法可以在不使用任何其他传感器信息的情况下实施，而在[1]中，作者使用了其他功能，例如声学或发音信息来改善基于EEG的连续语音识别系统的性能。
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.
