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arXiv · 2107.13969

Significance of Speaker Embeddings and Temporal Context for Depression Detection

Sri Harsha Dumpala·Sebastian Rodriguez·Sheri Rempel·Rudolf Uher·Sageev Oore
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Abstract

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.

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Sri Harsha Dumpala, Sebastian Rodriguez, Sheri Rempel, Rudolf Uher, Sageev Oore. 2021-07-24. Significance of Speaker Embeddings and Temporal Context for Depression Detection. https://arxiv.org/abs/2107.13969

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