BACKGROUND: Depression is a mental health condition that affects millions of people worldwide. Although common, it remains difficult to diagnose due to its heterogeneous symptomatology. Mental health questionnaires are currently the most used assessment method to screen depression; these, however, have a subjective nature due to their dependence on patients' self-assessments. Researchers have been interested in finding an accurate way of identifying depression through an objective biomarker. Recent developments in neural networks and deep learning have enabled the possibility of classifying depression through the computational analysis of voice recordings. However, this approach is heavily dependent on the availability of datasets to train and test deep learning models, and these are scarce. There are also very few languages available. This study proposes a protocol for the collection of a new dataset for deep learning research on voice depression classification, featuring Spanish speakers, professional and smartphone microphones, and a high-quality recording standard.
OBJECTIVE: This work aims to create a high-quality voice depression dataset by recording Spanish speakers with a professional microphone and strict audio quality standards. The data are captured by a smartphone microphone as well for further research in the use of smartphone recordings for deep learning depression classification.
METHODS: Our methodology involves the strategic collection of depressed and nondepressed voice recordings. A minimum participation of 60 subjects was established and 2 health centers were selected to gather data. A total of 3 types of data are collected: voice recordings, depression labels (using the Patient Health Questionnaire-9), and additional data that could potentially influence speech. Recordings are captured with professional-grade and smartphone microphones simultaneously to ensure versatility and practical applicability. Several considerations and guidelines are described to ensure high audio quality and avoid potential bias in deep learning research.
RESULTS: This data collection effort immediately enables new research topics on depression classification. Some potential uses include deep learning research on Spanish speakers, an evaluation of the impact of audio quality on developing audio classification models, and an evaluation of the applicability of voice depression classification technology on smartphone apps.
CONCLUSIONS: This research marks a significant step toward the objective and automated classification of depression in voice recordings. By focusing on the underrepresented demographic of Spanish speakers, the inclusion of smartphone recordings, and addressing the current data limitations in audio quality, this study lays the groundwork for future advancements in deep learning-driven mental health diagnosis.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60439.
