论文标题
从声学特征生成脑电图
Generating EEG features from Acoustic features
论文作者
论文摘要
在本文中，我们证明了使用基于复发性神经网络（RNN）的回归模型和生成对抗性网络（GAN）的声学特征预测脑脑磁性（EEG）的特征。我们从声学特征预测各种类型的脑电图特征。我们将结果与先前研究的语音合成问题进行比较，我们的结果表明，与使用相同数据集进行测试时，与从EEG特征（IE：使用EEG使用EEG）生成EEG特征（IE：语音合成）相比，可以从具有较低的均方根误差（RMSE），归一化RMSE值的声学特征生成EEG特征。
In this paper we demonstrate predicting electroencephalograpgy (EEG) features from acoustic features using recurrent neural network (RNN) based regression model and generative adversarial network (GAN). We predict various types of EEG features from acoustic features. We compare our results with the previously studied problem on speech synthesis using EEG and our results demonstrate that EEG features can be generated from acoustic features with lower root mean square error (RMSE), normalized RMSE values compared to generating acoustic features from EEG features (ie: speech synthesis using EEG) when tested using the same data sets.
