In this paper, we present an improved model for voicing silent speech, where
audio is synthesized from facial electromyography (EMG) signals. To give our
model greater flexibility to learn its own input features, we directly use EMG
signals as input in the place of hand-designed features used by prior work. Our
model uses convolutional layers to extract features from the signals and
Transformer layers to propagate information across longer distances. To provide
better signal for learning, we also introduce an auxiliary task of predicting
phoneme labels in addition to predicting speech audio features. On an open
vocabulary intelligibility evaluation, our model improves the state of the art
for this task by an absolute 25.8%.

本文提出了一种改进的模型，利用面部肌电图信号合成音频，并使用卷积层和 Transformer 层提取特征和传递信息，在预测语音音频特征的同时引入了辅助任务来提供更好的信号，实现了在开放词汇清晰度评估上表现出绝对提高了 25.8% 的最新技术水平。

提高了的无声语音识别模型

An Improved Model for Voicing Silent Speech

The link between different psychophysiological measures during emotion
episodes is not well understood. To analyse the functional relationship between
electroencephalography (EEG) and facial electromyography (EMG), we apply
historical function-on-function regression models to EEG and EMG data that were
simultaneously recorded from 24 participants while they were playing a
computerised gambling task. Given the complexity of the data structure for this
application, we extend simple functional historical models to models including
random historical effects, factor-specific historical effects, and
factor-specific random historical effects. Estimation is conducted by a
component-wise gradient boosting algorithm, which scales well to large data
sets and complex models.

研究了情感过程中不同心理生理学指标之间的联系，通过历史作用回归模型分析同时记录 24 位参与者在计算机化赌博任务中的 EEG 和 EMG 数据的函数关系，并扩展了包括随机历史效应、因素特定的历史效应和因素特定的随机历史效应的模型。使用分量回归梯度提升算法进行估计。