# SPEAK-SAFE: secure processing of electronic audio for knowledge in suicide assessment from therapeutic exchanges

## TL;DR

SPEAK-SAFE is a project that uses AI to analyze therapy sessions and improve suicide risk assessment while ensuring patient privacy.

## Contribution

The project introduces a secure end-to-end workflow for AI research in clinical contexts using a new German psychiatric corpus.

## Key findings

- A German psychiatric corpus is being developed for multimodal and NLP model training and evaluation.
- Pseudonymization is used to ensure patient privacy in the collected therapist-patient dialogue data.
- The project addresses challenges in securing patient privacy and ensuring data quality for AI analysis.

## Abstract

For therapists, the spoken word of their patients is among the most important foundations for clinical assessment. At the same time, it is hardly possible to monitor patients continuously and closely in sufficient numbers, for example, to ongoingly assess the risk of suicide in therapeutical conversations. Natural Language Processing (NLP) involves the use of Artificial Intelligence (AI) to analyze human language. Combining it with AI speech processing methods, we obtain multimodal methods which can automatically process large volumes of speech and language data to extract diagnostic information and therefore support individualized treatment plans. Thus, in NLP/multimodal methods, we see the opportunity to significantly improve patient care.

The SPEAK-SAFE project, implemented by clinicians and clinical researchers from the University hospital in Frankfurt in collaboration with the AI experts from the TU Darmstadt, aims to create the first German psychiatric corpus for evaluating and developing multimodal and NLP models to optimize diagnostic processes in psychiatric, psychosomatic, and psychotherapeutic care. Therefore, we will collect therapist-patient dialogues during therapy sessions. This sensitive data necessitates robust privacy. To meet this requirement, all collected data is pseudonymized, to ensure that no personal data is part of the evaluation and training of the AI models.

During the implementation of our research project, we were faced with challenges regarding the security of patient privacy and the technical implementation of therapy recordings toreassure sufficient data quality for the data analysis. Therefore, in addition to improve the suicidality prediction with multimodal methods we will develop an end-to-end-workflow for further AI-research in the clinical context.

Clinical Trial Registration: https://drks.de/search/de/trial/DRKS00027878, identifier DRKS00027878.