Assessing speaker independence on a speech-based depression level estimation system

Depression can be considered a psychological state related soft biometric traitSpeaker dependence of an iVector based depression level estimation system is assessedSystem performance is much better when the test speaker is on the training setExperimental frameworks must be carefully designed to avoid biasing the experimentsWe introduce a new metric for assessing depression classification systems Soft biometrics refers to traits that provide valuable information about an individual without being sufficient for their authentication, as they lack uniqueness and distinctiveness. This definition includes features related to the psychological state of individuals, such as emotions or mental health disorders like depression. Depression has recently been attracting the attention of speech researchers, with audio/visual emotion challenge (AVEC) 2013 and 2014 organized to encourage researchers to develop approaches to accurately estimate speaker depression level. The evaluation frameworks provided for these evaluations do not take speaker independence into account in experiment design, despite this being an important factor in developing a robust speech based system. We assess the influence of prior knowledge of the speakers in a depression estimation experiment, using an iVector-based state-of-the-art approach to depression level estimation to perform a speaker-dependent experiment and a speaker-independent experiment. We conclude that having previous information about the depression level of a given speaker dramatically improves system performance. Hence, we suggest that experimental frameworks must be carefully designed in order to serve as a genuinely useful resource for the development of robust depression estimation systems.