Speech sounds are produced as the coordinated movement of the speaking
organs. There are several available methods to model the relation of
articulatory movements and the resulting speech signal. The reverse problem is
often called as acoustic-to-articulatory inversion (AAI). In this paper we have
implemented several different Deep Neural Networks (DNNs) to estimate the
articulatory information from the acoustic signal. There are several previous
works related to performing this task, but most of them are using
ElectroMagnetic Articulography (EMA) for tracking the articulatory movement.
Compared to EMA, Ultrasound Tongue Imaging (UTI) is a technique of higher
cost-benefit if we take into account equipment cost, portability, safety and
visualized structures. Seeing that, our goal is to train a DNN to obtain UT
images, when using speech as input. We also test two approaches to represent
the articulatory information: 1) the EigenTongue space and 2) the raw
ultrasound image. As an objective quality measure for the reconstructed UT
images, we use MSE, Structural Similarity Index (SSIM) and Complex-Wavelet SSIM
(CW-SSIM). Our experimental results show that CW-SSIM is the most useful error
measure in the UTI context. We tested three different system configurations: a)
simple DNN composed of 2 hidden layers with 64x64 pixels of an UTI file as
target; b) the same simple DNN but with ultrasound images projected to the
EigenTongue space as the target; c) and a more complex DNN composed of 5 hidden
layers with UTI files projected to the EigenTongue space. In a subjective
experiment the subjects found that the neural networks with two hidden layers
were more suitable for this inversion task.

本研究采用深度神经网络方法，通过分析声音信号，逆推计算日常生活中使用的超声成像技术下的舌头构造，研究采用特征点空间和原始超声图像两种方式表征舌头构造信息的效果，并用 MSE、SSIM 等质量指标对结果进行测试与总结。