DAILY MENTAL HEALTH MONITORING FROM SPEECH: A REAL-WORLD JAPANESE DATASET AND MULTI-TASK LEARNING ANALYSIS

抄録

Translating mental health recognition from clinical research into real-world application requires extensive data, yet existing emotion datasets are impoverished in terms of daily mental health monitoring, especially when aiming for self-reported anxiety and depression recognition. We introduce the Japanese Daily Speech Dataset (JDSD), a large in-the-wild daily speech emotion dataset consisting of 20,827 speech samples from 342 speakersand 54 hours of total duration. The data is annotated on the Depression and Anxiety Mood Scale (DAMS) -- 9 self-reported emotions to evaluate mood state including ``vigorous'', ``gloomy'', ``concerned'', ``happy'', ``unpleasant'', ``anxious'', ``cheerful'', ``depressed'', and ``worried''. Our dataset possesses emotional states, activity, and time diversity, making it useful for training models to track daily emotional states for healthcare purposes. We partition our corpus and provide a multi-task benchmark across nine emotions, demonstrating that mental health states can be predicted reliably from self-reports with a Concordance Correlation Coefficient value of .547 on average.