Increasing trust & reliability of AI sign language translation

全世界有4.66亿人患有听力损失。我们提出的解决方案有助于通过提供英国手语（BSL）的实时翻译来打破沟通障碍。我们的项目是一个移动的应用程序，使用基于视觉的机器学习（ML）来翻译BSL。我们的创新在于使用这项技术，并创造性地训练一个强大的ML模型，以支持它，以提高听力障碍人士的可访问性。迄今为止，ML系统的有限可靠性并没有阻碍手语应用程序的无障碍解决方案的开发。由于BSL是一种复杂的3D语言，因此ML应用程序的鲁棒性缺乏-手势，身体姿势，面部表情和上下文都影响了符号的选择，其中个人偏见（口音）和地区方言也同样适用。由于缺乏足够的数据库来为通用BSL翻译器训练ML，这进一步加剧了这一问题。缺乏准确可靠的ML系统也导致用户缺乏信任和合规性。作为以人为本的设计师，我们感受到了这种挫折感，并欣赏边缘化群体无法获得的直观和可访问的用户体验。这一领域缺乏技术发展意味着当前的解决方案提供商诉诸于亲自使用人工口译员或作为视频会议的按需服务。这是非常昂贵的，并限制了听障人士的自由，并最终限制了他们的生活质量。我们的应用程序有可能用于广泛的用例，但为了应对和限制技术挑战，我们首先专注于咖啡店的订购。例如，它可以用于餐馆，零售，教育和任何视频会议软件，以帮助人们与听力障碍者进行沟通和理解。我们已完全准备好在2023年6月1日之前开始，并计划在6个月内提供一个无障碍，以人为本，一个用例的ML应用程序和一个使用训练数据的计划-我们自己的和开源语料库-以提供理想的体验。这种验证和曝光将使我们能够随着公司采用该技术并更普遍地发展业务而将该技术扩展到其他用例。简而言之，目前的挑战是目前可用的工具无法可靠地翻译手语。在这个项目中，我们将构建工具来有效地训练ML模型。

466 million worldwide have hearing loss. Our proposed solution helps to break down communication barriers by providing a real-time translation of British Sign Language (BSL). Our project is a mobile application that uses vision-based machine learning (ML) to translate BSL. Our innovation lies in the use of this technology and creatively training a robust ML model that underpins it to improve accessibility for people with hearing-impairments.To date, the limited reliability of ML systems has not stunted the development of accessible solutions for sign language applications. Since BSL is a complex, 3D language the robustness of ML applications is lacking - hand gestures, body posture, facial expression and the context all influence the choice of signs where individual bias (accent) and regional dialect similarly apply. This is further compounded by an insufficient corpus of data to train the ML for a general-purpose BSL translator.The lack of accurate and reliable ML systems also leads to a lack of trust and compliance from users. As human-centred designers, we feel this frustration and appreciate intuitive and accessible user experiences that are not available for marginalised groups.The lack of technical development in this area means current solution providers resort to using human interpreters in person or as an on-demand service over video conferencing. This is very costly and restricts the freedom of hearing-impaired individuals and ultimately, their quality of life.Our application has the potential to be used for a wide range of use cases, but to stage-gate and constrain the technical challenge, we first focus on ordering in coffee shops. For example, it could be used in restaurants, retail, education, and any video conferencing software to help people communicate with and understand those with hearing-impairments.We are fully ready to begin before the 1st June 2023 and over 6 months plan to deliver an accessible, human-centric, ML application for one use case and a plan for to use the training data - both our own and open-source corpora - to deliver a desirable experience. This validation and exposure will enable us to grow the technology to other use cases as companies adopt the technology and grow the business more generally.In short, the current challenge is that the tools available at the moment aren't capable of reliably translating sign language. In this project, we are going to build the tools to effectively train an ML model.