论文标题
预测脑电图和直接合成脑电图的不同声学特征
Predicting Different Acoustic Features from EEG and towards direct synthesis of Audio Waveform from EEG
论文作者
论文摘要
在[1,2]中，作者为脑电图（EEG）特征的综合语音提供了初步结果，他们首先预测EEG特征的声学特征，然后使用Griffin Lim重构算法从预测的声学特征中重建语音。在本文中，我们首先引入了一个深度学习模型，该模型将原始的EEG波形信号作为输入，并直接产生音频波形作为输出。然后，我们演示了来自脑电图特征的16种不同的声学特征。我们在本文中证明了口语和聆听条件的结果。本文介绍的结果表明，不同的声学特征与语音感知和生产过程中记录的非侵入性神经脑电图信号是如何相关的。
In [1,2] authors provided preliminary results for synthesizing speech from electroencephalography (EEG) features where they first predict acoustic features from EEG features and then the speech is reconstructed from the predicted acoustic features using griffin lim reconstruction algorithm. In this paper we first introduce a deep learning model that takes raw EEG waveform signals as input and directly produces audio waveform as output. We then demonstrate predicting 16 different acoustic features from EEG features. We demonstrate our results for both spoken and listen condition in this paper. The results presented in this paper shows how different acoustic features are related to non-invasive neural EEG signals recorded during speech perception and production.
