Abstract
No studies have investigated cross-cultural and cross-language characteristics of depressed speech. We investigated the generalisability of a vocal biomarker-based approach to depression detection in clinical interviews recorded in three countries (Australia, the USA and Germany), two languages (German and English) and different accents (Australian and American). Several approaches to training and testing within and between datasets were evaluated. Using the same experimental protocol separately within each dataset, (cross-classification) accuracy was high. Combining datasets, high accuracy was high again and consistent across language, recording environment, and culture. Training and testing between datasets, however, attenuated accuracy. These finding emphasize the importance of heterogeneous training sets for robust depression detection.