Machine learning models in ultrasound tongue imaging for the detection of children's speech disorders

目前在言语语言治疗（SLT）中，技术支持很少。语言障碍的评估特别耗时，而且缺乏技术解决方案。通常情况下，言语评估包括听孩子说话并写下他们所说的话。这种方法可能会忽略孩子说话方式中重要的微妙之处。例如，一个孩子可能会试图说“key”，而它可能被听成“tea”。这导致SLT认为孩子无法分辨t和k之间的区别，并选择旨在解决这个问题的治疗方法。然而，使用医学超声成像舌头运动显示，在许多情况下，儿童产生了难以察觉的错误。这对于唇腭裂儿童来说尤其如此，他们会产生各种不同寻常的语言错误。然而，超声波分析是一项耗时的任务，只能由受过专业训练的语言科学家来完成。本项目旨在利用机器学习方法对唇腭裂儿童的舌形进行分类。这可能对评估和衡量干预措施的进展都有用。来自唇腭裂儿童的开源数据是可用的，NHS大格拉斯哥和克莱德正在进行的临床项目目前正在收集进一步的超声数据。

Currently in Speech and Language Therapy (SLT), technological support is sparse. Assessment of speech disorders is particularly time consuming and suffers from a lack of technological solutions. Normally speech assessment involves listening to the child and writing down what they say. This approach can miss important subtleties in the way children speak. For example, a child may try to say "key" and it may be heard as "tea". This leads the SLT to believe the child cannot tell the difference between t and k and select a therapy designed to tackle this. However, using medical ultrasound to image tongue movements reveals that in many cases children are producing imperceptible errors. This is particularly the case for children with cleft lip and palate who produces a wide variety of unusual speech errors. However, ultrasound analysis is a time consuming task which can only be carried out by a speech scientist with specialist training. This project aims to use machine learning approaches for method for classifying tongue shapes in children with cleft lip and palate. This has the potential to be useful both for assessment and for measuring progress in intervention. Open source data from children with cleft lip and palate is available and an ongoing clinical project with NHS Greater Glasgow and Clyde is currently collecting further ultrasound data.