  Ultrasound tongue imaging is widely used for speech production research, and
it has attracted increasing attention as its potential applications seem to be
evident in many different fields, such as the visual biofeedback tool for
second language acquisition and silent speech interface. Unlike previous
studies, here we explore the feasibility of age estimation using the ultrasound
tongue image of the speakers. Motivated by the success of deep learning, this
paper leverages deep learning on this task. We train a deep convolutional
neural network model on the UltraSuite dataset. The deep model achieves mean
absolute error (MAE) of 2.03 for the data from typically developing children,
while MAE is 4.87 for the data from the children with speech sound disorders,
which suggest that age estimation using ultrasound is more challenging for the
children with speech sound disorder. The developed method can be used a tool to
evaluate the performance of speech therapy sessions. It is also worthwhile to
notice that, although we leverage the ultrasound tongue imaging for our study,
the proposed methods may also be extended to other imaging modalities (e.g.
MRI) to assist the studies on speech production.
