Significance of Speaker Embeddings and Temporal Context for Depression Detection
This work addresses depression detection for mental health applications, but it appears incremental as it builds on existing methods by adding speaker-specific analysis.
The paper tackled depression detection from speech by analyzing the significance of speaker embeddings and temporal context, achieving state-of-the-art performance with improved results when combining speaker embeddings with conventional features.
Depression detection from speech has attracted a lot of attention in recent years. However, the significance of speaker-specific information in depression detection has not yet been explored. In this work, we analyze the significance of speaker embeddings for the task of depression detection from speech. Experimental results show that the speaker embeddings provide important cues to achieve state-of-the-art performance in depression detection. We also show that combining conventional OpenSMILE and COVAREP features, which carry complementary information, with speaker embeddings further improves the depression detection performance. The significance of temporal context in the training of deep learning models for depression detection is also analyzed in this paper.