SBIR Phase II: Automated Emotional Distress Severity Classification for Children and Adolescents Using Speech Emotion Recognition and AI

SBIR 第二阶段：使用语音情绪识别和人工智能对儿童和青少年进行自动情绪困扰严重程度分类

• 批准号: 2126811
• 负责人: Yared Alemu
• 金额: $ 93.26万
• 依托单位: TQIntelligence, Inc.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126811&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Automated; Emotional

The broader impact and commercial potential of this Small Business Innovation Research (SBIR) Phase II project will leverage the emerging science of Speech Emotion Recognition and artificial intelligence (AI) to transform the delivery of personalized behavioral healthcare services for children and adolescents from low-income communities. A strong need exists for a platform to measure mental disorders objectively and enable collaboration between multiple stakeholders for affordable quality mental healthcare. This project advances a talk therapy software that extracts voice biomarkers from 60-second speech samples to measure the severity of emotional distress for children and adolescents. The platform’s voice-based algorithm augments therapists' clinical capabilities – increasing the capacity of community mental health providers to address both quality and the issue of access. The proposed project develops a voice-based biomarker algorithm trained to understand behavioral and emotional tendencies and anticipate future behaviors to determine if a child’s vocal utterances deviate from age-appropriate linguistic and speech patterns. The company is developing a proprietary clinical voice sample database and repository representing marginalized communities (African American, Latino, and Caucasian within rural communities) that will exceed in both volume and accuracy those of its industry peers. Today, the company’s database consists of a diverse, foundational set and size of voice samples essential for developing a more accurate algorithm(s) to detect and predict emotional disorder severity. The proposed research within Phase II builds on the progress achieved in Phase I, using speech emotion recognition and Machine Learning (ML) to identify measurable biomarkers for trauma and stress. The methodology links trauma, stress, and voice types.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.

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响和商业潜力将利用语音情感识别和人工智能（AI）的新兴科学，为低收入社区的儿童和青少年提供个性化的行为医疗服务。迫切需要一个平台来客观地衡量精神障碍，并使多个利益相关者之间能够合作，以获得负担得起的高质量精神保健。该项目提出了一种谈话治疗软件，该软件从60秒的语音样本中提取语音生物标志物，以测量儿童和青少年情绪困扰的严重程度。该平台基于语音的算法增强了治疗师的临床能力-提高了社区心理健康提供者解决质量和访问问题的能力。 该项目开发了一种基于语音的生物标志物算法，该算法经过训练，可以理解行为和情感倾向，并预测未来的行为，以确定儿童的发声是否偏离了与年龄相适应的语言和语音模式。该公司正在开发一个专有的临床语音样本数据库和存储库，代表边缘化社区（非洲裔美国人，拉丁美洲人和农村社区内的高加索人），在数量和准确性方面都将超过同行。今天，该公司的数据库包括一个多样化的，基本的语音样本集和大小，对于开发更准确的算法来检测和预测情绪障碍的严重程度至关重要。第二阶段的拟议研究建立在第一阶段取得的进展基础上，使用语音情感识别和机器学习（ML）来识别创伤和压力的可测量生物标志物。该奖项反映了NSF的法定使命，并被认为是值得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的支持。