Matthew P. Black

TL;DR: This work fastidiously utilizes ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools and highlight the limitations of current caregiver-report instruments.

TL;DR: The psychologist, acting as evaluator and interlocutor, was shown to adjust his or her behavior in predictable ways based on the child's social-communicative impairments, and the results support future study of speech prosody of both interaction partners during spontaneous conversation, while using automatic computational methods that allow for scalable analysis on much larger corpora.

TL;DR: In this paper, a large corpus of married couples' problem-solving interactions was analyzed and each spouse was manually coded with multiple session-level behavioral observations (e.g., level of blame toward other spouse), and acoustic speech features were used to automatically classify extreme instances for six selected codes.

TL;DR: Two separate systems for predicting depression levels and affective dimensions are developed, and for both problems, the proposed systems outperform the video-feature based baseline systems.