PROJECT SUMMARY/ABSTRACT This administrative supplemental funding request is linked with NIMH Supported R01MH116902 and is responsive to NOT-OD-21-094 to Support Collaborations to Improve AI/ML-Readiness of NIH Supported Data. The supplement project builds a new collaboration with a computer scientist team at University of Illinois Chicago that focuses on natural language processing. The central aim of the project is to create and share a novel database resource for multi-modal speech analysis from a unique, large, and diverse sample of audio recorded standardized social interactions. Social processes are a key dimension of dysfunction in serious mental illness. The linguistic and paralinguistic markers of impairment in serious mental illness and suicide are an active area of research, consistent with the Parent Study’s focus on social processes in suicide and psychosis. However, small datasets and lack of standardization of data resources greatly hamper extant speech analysis work, disenabling investigation of mechanisms underlying observed patterns and robustness of models across potential sources of bias. In the proposed supplement, we will generate a new corpus of over 1200 cases who have standardized data for speech analysis, are richly characterized, and who are diverse across key demographic variables including minority status. Our focus is on the Social Skills Performance Assessment, which is the most widely used performance-based measure of social function, entailing expert-rated, audio recorded, simulated social interactions that involve affiliative and confrontational scenarios. In the proposed supplement, we will generate sharable, de-identified transcripts for natural language processing, with additional annotation according to conventional natural language features and also more novel dialogue actions. We will also create corresponding de-identified audio files that contain frequency, amplitude and other para-linguistic annotations. This project extends and expands parallel work in aging research led by our new collaborator team. Indeed, sharable data from speech analysis derived from standardized testing has been highly impactful in other fields, including aging and dementia research, but currently no such data resource exists in large samples of people with mental illnesses. The Aims of the project are to create, process and annotate the dataset, generate toolkits and source code for analysis, examine new markers in relation to the Parent Study Aims, and share the data to the NIMH National Data Archive and the scientific community.