SBIR Phase II: Evaluative Artificial Speech Intelligence and Autism Screener (EASI-AS)

这一小型企业创新研究(SBIR)第二阶段项目的更广泛影响将改善对沟通障碍儿童的治疗。该项目将开发一个系统，为专家和家长提供准确、高效、易于理解的演讲和语言评估报告，并提供建议和建议，以更好地了解儿童的沟通挑战以及如何解决这些挑战。它将弥合临床评估和家庭支持之间的差距，通过对儿童语言发展需求提供更准确的评估，影响临床结果。因此，这将导致更个性化的教育和治疗，特别是对不同的社区。这个小型企业创新研究(SBIR)第二阶段项目解决了与沟通障碍儿童的言语和语言评估有关的痛点。必须在自然环境中捕捉儿童的语言使用情况，以获得准确的分析。该解决方案是一个灵活的、自我定制的系统，使用训练有素的人工智能方法来评估语音和语言技能，然后在关键沟通领域提供分析反馈。为了评估儿童的交流技能，该系统收集了基于成人-儿童自然社交语言互动的语言样本。研究目标包括：(1)改进和开发机器学习模块，特别是语言水平评估、推荐引擎、儿童声音识别、口音和方言识别以及语调检测；(2)自闭症筛查开发，用作眼球跟踪技术的预诊断工具；(3)网络和移动应用程序开发，用于儿童数据收集。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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 II project will improve treatment for children with communication impairments. The project will develop a system that provides specialists and parents with reports that have accurate, efficient, and easy-to-understand speech and language evaluations with recommendations and suggestions, for better understanding the children’s communication challenges and how to solve them. It will bridge the gap between a clinical assessment and support at home, impacting clinical outcomes by providing a more accurate assessment of child language developmental needs. Consequently, this will result in more personalized education and treatment, particularly for diverse communities. This Small Business Innovation Research (SBIR) Phase II project addresses pain points surrounding speech and language assessment with children with communication impairments. The child’s language use must be captured in a natural setting to obtain an accurate analysis. The solution is a flexible, self-customizing system using a trained AI approach to evaluate speech and language skills, then provide analytical feedback in key communication areas. To evaluate a child’s communication skills, the system collects a language sample based on natural adult-child social language interactions. Research objectives include: (1) improvement and development of machine-learning modules, specifically the language proficiency assessment, recommendation engine, recognition of child voices, recognition of accents and dialects, and intonation detection; (2) an autism screener development, to be used as a pre-diagnostic tool with eye-tracking technology; and (3) web and mobile application development for data collection by the child.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.