A systematic review of the application of machine learning techniques to ultrasound tongue imaging analysis

Abstract

B-mode ultrasound has emerged as a prevalent tool for observing tongue motion in speech production, gaining traction in speech therapy applications. However, the effective analysis of ultrasound tongue image frame sequences (UTIFs) encounters many challenges, such as the presence of high levels of speckle noise and obscured views. Recently, the application of machine learning, especially deep learning techniques, to UTIF interpretation has shown promise in overcoming these hurdles. This paper presents a thorough examination of the existing literature, focusing on UTIF analysis. The scope of our work encompasses four key areas: a foundational introduction to deep learning principles, an exploration of motion tracking methodologies, a discussion of feature extraction techniques, and an examination of cross-modality mapping. The paper concludes with a detailed discussion of insights gleaned from the comprehensive literature review, outlining potential trends and challenges that lie ahead in the field.