Leveraging ordinal information for speech-based depression classification

Abstract

While depression is inherently ordinal, much of the previous work in depression detection oversimplifies the problem by treating it as a binary classification problem, ignoring the subtle variations and the order in depression severity. We propose creating a latent space that contains ordinal information via an ordinal loss to benefit the learning of depression classification. Specifically, we define K thresholds for the depression scores, thereby creating a series of binary classification tasks on different levels of depression (e.g., mild vs. non-mild). The ordinal loss allows the model to capture the relationships between these levels on top of the binary classification task. Our approach outperforms current state-of-the-art depression detection methods, highlighting the importance of considering the inherent ordinal nature of depression severity.