# Significance of Speaker Embeddings and Temporal Context for Depression Detection

arXiv: https://arxiv.org/abs/2107.13969 · HTML: https://mlsanity.com/paper/2107.13969/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 24 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019)
- [AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge](https://mlsanity.com/paper/1605.01600/) (2016)
- [x-vectors meet emotions: A study on dependencies between emotion and speaker recognition](https://mlsanity.com/paper/2002.05039/) (2020)
- [Automated speech-based screening of depression using deep convolutional neural networks](https://mlsanity.com/paper/1912.01115/) (2019)

## Abstract (arXiv)

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.