Thanks to the latest deep learning algorithms, silent speech interfaces (SSI)
are now able to synthesize intelligible speech from articulatory movement data
under certain conditions. However, the resulting models are rather
speaker-specific, making a quick switch between users troublesome. Even for the
same speaker, these models perform poorly cross-session, i.e. after dismounting
and re-mounting the recording equipment. To aid quick speaker and session
adaptation of ultrasound tongue imaging-based SSI models, we extend our deep
networks with a spatial transformer network (STN) module, capable of performing
an affine transformation on the input images. Although the STN part takes up
only about 10\% of the network, our experiments show that adapting just the STN
module might allow to reduce MSE by 88\% on the average, compared to retraining
the whole network. The improvement is even larger (around 92\%) when adapting
the network to different recording sessions from the same speaker.

利用深度学习算法从口腔运动数据中合成出可理解的语音，但相关模型区分度较高，对不同用户快速切换不便。该论文中利用空间变换网络（STN）模块扩展深度网络提高使用噪声下的语音接口模型的适应性，该方法有效降低了标准误差并提高了同一发言者不同录音周期的性能。