Real-time Tongue from Ultrasound

超声波实时舌头

• 批准号: RGPIN-2018-06830
• 负责人: Lee, WonSook
• 金额: $ 1.68万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=657078
• 关键词: Real; time; Tongue; Ultrasound

***The motivation for our research is to develop a tongue visualization tool to help people who require speech therapy, who learn foreign languages, or who otherwise wish to “see how they talk”. The tongue is the most important articulator of speech and a large proportion of the research on tongue movement focuses on its role in speech. Many aural training applications exist but visual training is not mature. Unfortunately, the tongue is an organ difficult to see and hard to track using conventional camera or optical motion trackers. This has led to tongue motion visualization using MRI and ultrasound machines. The quality of MRI images is impressive, but it is prohibitively expensive and generally operates only in a noisy and constrained environment. While ultrasound does not have as high quality images, it is increasingly popular in phonetics research and medical imaging due to strong advantages such as non-invasive measurement, affordable equipment, portability, and real-time display of the tongue. We propose to use deep learning and other recent machine-learning techniques in developing a new algorithm for efficient, robust and real-time tongue segmentation from ultrasound imaging of the tongue in motion. This will lead to 3D tongue reconstruction and simulation, based only on a single sagittal view angle of the tongue captured by a ultrasound probe. We have previously performed static 3D reconstructions from semi-automatically segmented tongue ultrasound images but the present proposal focuses on real-time tongue segmentation. The result will provide visual feedback of mispronunciation through comparison with the tongue motion of a standard language speaker.

* 我们研究的动机是开发一种舌头可视化工具，以帮助需要语言治疗的人，学习外语的人，或者希望“看看他们如何说话”的人。舌头是言语中最重要的发音器官，对舌头运动的研究主要集中在它在言语中的作用上。许多听觉训练应用程序存在，但视觉训练还不成熟。不幸的是，舌头是一个很难看到的器官，很难使用传统的相机或光学运动跟踪器进行跟踪。这导致了使用MRI和超声机器的舌头运动可视化。 MRI图像的质量令人印象深刻，但它过于昂贵，通常只能在嘈杂和受限的环境中运行。虽然超声波没有高质量的图像，但由于其强大的优势，如无创测量，负担得起的设备，便携性和舌头的实时显示，它在语音研究和医学成像中越来越受欢迎。我们建议使用深度学习和其他最新的机器学习技术来开发一种新的算法，用于从运动中的舌头的超声成像中进行高效，鲁棒和实时的舌头分割。 这将导致仅基于由超声探头捕获的舌头的单个矢状视角的3D舌头重建和模拟。我们以前进行了静态三维重建半自动分割的舌超声图像，但目前的建议侧重于实时舌分割。其结果将通过与标准语言说话者的舌头运动进行比较来提供发音错误的视觉反馈。