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

Tqintelligence, Inc., Tucker GA

Investigators

Abstract

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.