论文标题
使用脑电图推进语音综合
Advancing Speech Synthesis using EEG
论文作者
论文摘要
在本文中，我们介绍了注意力回归模型，以演示与口语句子并行记录的脑电图（EEG）特征预测声学特征。首先，我们使用我们的注意力模型展示了直接从脑电图特征中预测的声学特征，然后我们使用两步方法展示了从脑电图中预测脑电图特征的声学特征，在第一步中，我们使用注意力模型来预测EEG特征的关节功能，然后在第二步中，另一个注意力回归模型训练了预测的表达功能，可以将预测的表达功能转化为声学特征。我们提出的注意力回归模型与[1]中作者在测试期间使用其大多数受试者的数据集进行测试时，在[1]中引入的回归模型相比，表现出了卓越的性能。本文提出的结果进一步推动了[1]中作者描述的工作。
In this paper we introduce attention-regression model to demonstrate predicting acoustic features from electroencephalography (EEG) features recorded in parallel with spoken sentences. First we demonstrate predicting acoustic features directly from EEG features using our attention model and then we demonstrate predicting acoustic features from EEG features using a two-step approach where in the first step we use our attention model to predict articulatory features from EEG features and then in second step another attention-regression model is trained to transform the predicted articulatory features to acoustic features. Our proposed attention-regression model demonstrates superior performance compared to the regression model introduced by authors in [1] when tested using their data set for majority of the subjects during test time. The results presented in this paper further advances the work described by authors in [1].
