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

这个小企业创新研究（SBIR）第二阶段项目的更广泛影响将改善沟通障碍儿童的治疗。该项目将开发一个系统，为专家和家长提供报告，这些报告具有准确，高效和易于理解的语音和语言评估以及建议和建议，以便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，通过提供对儿童语言发展需求的更准确评估来影响临床结果。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。 这个小企业创新研究（SBIR）第二阶段项目解决了沟通障碍儿童言语和语言评估的痛点。儿童的语言使用必须在自然的环境中捕捉，以获得准确的分析。该解决方案是一个灵活的自定义系统，使用经过训练的人工智能方法来评估语音和语言技能，然后在关键的沟通领域提供分析反馈。为了评估儿童的沟通技巧，该系统收集了基于自然的成人与儿童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，特别是语言能力评估、推荐引擎、儿童语音识别、口音和方言识别以及语调检测;（2）开发自闭症筛查工具，用作眼动跟踪技术的预诊断工具;以及（3）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进行评估，被认为值得支持审查标准。

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.