Improving Ultrasound Tongue Image Reconstruction from Lip Images Using Self-supervised Learning and Attention Mechanism

TL;DR

This paper introduces a self-supervised learning approach with attention mechanisms to predict ultrasound tongue images from lip videos, enhancing understanding of speech production dynamics.

Contribution

The study presents a novel self-supervised framework combining two-stream CNNs, LSTM, and attention to reconstruct tongue images from lip videos without labeled data.

Findings

Generated tongue images closely match real ultrasound images

Achieved effective modality matching between lip videos and ultrasound images

Demonstrated the potential for unsupervised speech production modeling

Abstract

Speech production is a dynamic procedure, which involved multi human organs including the tongue, jaw and lips. Modeling the dynamics of the vocal tract deformation is a fundamental problem to understand the speech, which is the most common way for human daily communication. Researchers employ several sensory streams to describe the process simultaneously, which are incontrovertibly statistically related to other streams. In this paper, we address the following question: given an observable image sequences of lips, can we picture the corresponding tongue motion. We formulated this problem as the self-supervised learning problem, and employ the two-stream convolutional network and long-short memory network for the learning task, with the attention mechanism. We evaluate the performance of the proposed method by leveraging the unlabeled lip videos to predict an upcoming ultrasound tongue image sequence. The results show that our model is able to generate images that close to the real ultrasound tongue images, and results in the matching between two imaging modalities.