Abstract
Ultrasound tongue imaging (UTI) is a non-invasive and cost-effective tool for studying speech articulation, motor control, and related disorders. However, real-time tongue contour segmentation remains challenging due to low signal-to-noise ratios, imaging variability, and computational demands. We propose UltraUNet, a lightweight encoder-decoder architecture optimized for real-time segmentation of tongue contours in ultrasound images. UltraUNet incorporates domain-specific innovations such as lightweight Squeeze-and-Excitation blocks, Group Normalization for small-batch stability, and summation-based skip connections to reduce memory and computational overhead. It achieves 250 frames per second and integrates ultrasound-specific augmentations like denoising and blur simulation. Evaluations on 8 datasets demonstrate high accuracy and robustness, with single-dataset Dice = 0.855 and MSD = 0.993px, and cross-dataset Dice averaging 0.734 and 0.761. UltraUNet provides a fast, accurate solution for speech research, clinical diagnostics, and analysis of speech motor disorders.
Abstract (translated)
超声舌成像（UTI）是一种用于研究语音发音、运动控制及相关障碍的非侵入性和成本效益高的工具。然而，由于信号噪声比低、成像变化大以及计算需求高，实时舌轮廓分割仍然具有挑战性。我们提出了UltraUNet，这是一种轻量级编码器-解码器架构，旨在实现实时超声图像中舌轮廓的分割。UltraUNet结合了领域特定创新，如轻量级挤压和激励（Squeeze-and-Excitation）块、用于小批量稳定性的组归一化以及基于求和的跳跃连接以减少内存和计算开销。该模型可实现每秒250帧的速度，并集成了超声波特有的增强技术，例如去噪和模糊模拟。在8个数据集上的评估表明其具有高准确性和鲁棒性，在单数据集中Dice系数为0.855、平均表面距离（MSD）为0.993像素；跨数据集的Dice系数分别为0.734和0.761。UltraUNet为语音研究、临床诊断以及对语音运动障碍的分析提供了一个快速且准确的解决方案。
URL
https://arxiv.org/abs/2509.23225
