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Depression detection from speech has attracted a lot of attention in recentyears. However, the significance of speaker-specific information in depressiondetection has not yet been explored. In this work, we analyze the significanceof speaker embeddings for the task of depression detection from speech.Experimental results show that the speaker embeddings provide important cues toachieve state-of-the-art performance in depression detection. We also show thatcombining conventional OpenSMILE and COVAREP features, which carrycomplementary information, with speaker embeddings further improves thedepression detection performance. The significance of temporal context in thetraining of deep learning models for depression detection is also analyzed inthis paper.

Significance of Speaker Embeddings and Temporal Context for Depression Detection