Test-Time Training for Depression Detection
Sri Harsha Dumpala Chandramouli Shama Sastry Rudolf Uher Sageev Oore
Previous works on depression detection use datasets collected in similar
environments to train and test the models. In practice, however, the train and
test distributions cannot be guaranteed to be identical. Distribution shifts
can be introduced due to variations such as recording environment (e.g.,
background noise) and demographics (e.g., gender, age, etc). Such
distributional shifts can surprisingly lead to severe performance degradation
of the depression detection models. In this paper, we analyze the application
of test-time training (TTT) to improve robustness of models trained for
depression detection. When compared to regular testing of the models, we find
TTT can significantly improve the robustness of the model under a variety of
distributional shifts introduced due to: (a) background-noise, (b) gender-bias,
and (c) data collection and curation procedure (i.e., train and test samples
are from separate datasets).