SBIR Phase I: Automated Speech Therapy Through Speech Recognition with Error Identification

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响是通过技术增强的语音治疗增加获得护理的机会，改善语音障碍（SSD）患者的预后。大约5%的儿童患有SSD，这使他们在成年后面临阅读困难和处理挑战的风险。需要言语治疗服务来改变言语产生技能，以提高日常生活功能活动中的可理解性和互动性。 然而，由于难以获得护理和费用，不到70%的儿童接受治疗。该项目开发人工智能，可以重新创建语音语言病理学家在现场治疗环境中做出的反馈决定。然后，这项技术可以应用于计算机和平板电脑提供的语音治疗。由于自动化，接受服务的成本可以大大降低，目前无法获得服务的儿童可以在家中获得高质量的自动化治疗。这个小型企业创新研究（SBIR）第一阶段项目将开发能够识别语音错误的机器学习算法，以实现自动化语音治疗。自动语音识别技术的进步已经导致了利用人工智能作为语音错误处理中的技术辅助努力的一个方面的探索。然而，现有的技术在其应用程序的语音治疗的可靠性是穷人由于受损的儿科语音数据库不足。本研究的目的是：1）建立一个八个最常出现错误的英语辅音的受损和准确产生的数据库; 2）使用这个数据库开发和训练识别分割目标音素的特定错误的算法;以及3）利用来自训练语音的决策完成训练算法的评分者间可靠性测试。语言病理学家，以确保充分的协议存在的错误和错误类型。通过创建一个能够完成这些类型任务的新型训练数据库和算法，人工智能可以进一步重建语音语言病理学家在现场治疗环境中做出的特定于错误的辨别和反馈决策，作为一个迫切需要的工具，以解决障碍，从事足够频繁的语音生产实践会议。这个奖项反映了NSF的法定使命并通过使用基金会的知识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 outcomes for individuals with speech sound disorders (SSD) by increasing access to care through technology augmented speech therapy. Approximately 5% of children have a SSD, which places them at risk for reading difficulties and processing challenges into adulthood. Speech therapy services are needed to create changes in speech production skills for improved intelligibility and interaction in functional activities of daily living. However, less than 70% of children receive therapy due to difficulty with accessing care and cost. This project develops artificial intelligence that can recreate the feedback decisions that a speech-language pathologist makes in a live therapy setting. This technology can then be applied to computer- and tablet-delivered speech therapy. Because of the automation, the cost of receiving services can be drastically reduced and children currently without access can get high quality automated therapy at home. This Small Business Innovation Research (SBIR) Phase I project will develop machine learning algorithms capable of speech sound error identification for the purposes of automating speech therapy. Advances in Automatic Speech Recognition technology have led to the exploration of harnessing artificial intelligence as an aspect of technology-assisted endeavors in speech sound error treatment. However, reliability of the existing technology in its application to speech therapy is poor due to inadequate databases of impaired pediatric speech. The objectives of this study are to: 1) Build a database of impaired and accurate productions of the eight English consonants most commonly in error; 2) use this database to develop and train algorithms for identifying specific errors of segmented target phonemes; and 3) complete inter-rater reliability tests of the trained algorithm with decisions from trained speech-language pathologists to ensure adequate agreement of presence of error and error type. By creating a novel training database and algorithm that has the ability to accomplish these types of tasks, artificial intelligence can advance to recreate the error-specific discrimination and feedback decisions that a speech-language pathologist makes in a live therapy setting, serving as a critically needed tool for addressing the barriers to engaging in adequately frequent speech sound production practice sessions.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.