Hierarchical Attention Transfer Networks For Depression Assessment From Speech

A growing area of mental health research is the search for speech-based objective markers for conditions such as depression. However, when combined with machine learning, this search can be challenging due to a limited amount of annotated training data. In this paper, we propose a novel crosstask approach which transfers attention mechanisms from speech recognition to aid depression severity measurement. This transfer is applied in a two-level hierarchical network which mirrors the natural hierarchical structure of speech. Experiments based on the Distress Analysis Interview Corpus–Wizard of Oz (DAIC-WOZ) dataset, as used in the 2017 Audio/Visual Emotion Challenge, demonstrate the effectiveness of our Hierarchical Attention Transfer Network. On the development set, the proposed approach achieves a root mean square error (RMSE) of 3.85, and a mean absolute error (MAE) of 2.99, on a Patient Health Questionnaire (PHQ)-8 scale [0, 24], while on the test set, it achieves an RMSE of 5.66 and an MAE of 4.28. To the best of our knowledge, these scores represent the best-known speech-only results to date on this corpus.