SBIR Phase I: Real-Time Artificial Intelligence (AI) Bidirectional American Sign Language (ASL) Communication System

这个小型企业创新研究（SBIR）第一阶段项目的更广泛影响是通过自动手语识别改善聋人和听力障碍（D/HH）个人与听力社区之间的沟通。仅在美国就有超过4800万D/HH个人，他们总共拥有870亿美元的购买力。看来企业没有充分服务于这个社区，正如美国残疾人法案（ADA）对许多公司提起的诉讼所证明的那样。拟议的技术将为组织提供即插即用软件，以改善他们与D/HH个人的互动。企业和政府将能够与他们的D/HH员工，客户或选民互动时，口译不可用。这项技术可以集成到各种平台中，从零售点销售设备到聊天机器人和视频/电话会议系统。这个小型企业创新研究（SBIR）第1阶段项目旨在开发技术，以执行无约束的手语识别和自然手语制作。具体地说，目前训练语言翻译模型的方法由于缺乏该领域内的训练数据而无法处理手语领域。此外，用于产生美国手语（ASL）的所有当前建立的方法（除了不可缩放的运动捕捉之外）导致来自化身的生硬的、不自然的签名。该项目将通过半监督专家增强模型和数据增强技术在ASL领域内开发这些问题的解决方案。技术障碍包括缺乏处理高维低资源语言域的模型，以及缺乏足够大的数据集。技术里程碑包括创建半监督数据集，工程数据增强技术，生成自然签名头像，以及执行广泛的可用性测试。该项目旨在开发一种方法，用于在低资源手语和英语之间进行自动翻译，以改善聋人和听力困难社区的无障碍环境并增加公平性。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to improve the communication between Deaf and Hard of Hearing (D/HH) individuals and the hearing community through automated sign language recognition. In the United States alone there are over 48 million D/HH individuals, who in total possess $87 billion in purchasing power. It appears businesses are not adequately serving this community, as is evidenced by the plethora of Americans with Disabilities Act (ADA) lawsuits against numerous companies. The proposed technology will provide plug-and-play software for organizations to improve their interactions with D/HH individuals. Businesses and governments will be able to interact with their D/HH employees, customers, or constituents when interpreters are unavailable. This technology can be integrated into a variety of platforms, from retail point-of-sale equipment to chatbots and video/teleconferencing systems.This Small Business Innovation Research (SBIR) Phase 1 project aims to develop technology to perform unconstrained sign language recognition and natural sign language production. Specifically, current methods to train language translation models are ill-equipped to handle the sign language domain due to the lack of training data within this domain. Additionally, all currently established methods (apart from motion capture, which is unscalable) for producing American Sign Language (ASL) result in stilted, unnatural signing from an avatar. This project will develop solutions to these issues within the domain of ASL via semi-supervised expert-augmented models and data augmentation techniques. Technical hurdles include the lack of models to handle high-dimensional low-resource language domains, and lack of sufficiently large datasets. Technical milestones include creating semi-supervised datasets, engineering data augmentation techniques, generating a natural signing avatar, and performing extensive usability testing. This project aims to produce a method for automatically interpreting between a low-resource sign language and English to improve accessibility and increase equity for the Deaf and Hard of Hearing communities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.