# The Relationship Between Speech Features Changes When You Get Depressed: Feature Correlations for Improving Speed and Performance of Depression Detection

arXiv: https://arxiv.org/abs/2307.02892 · HTML: https://mlsanity.com/paper/2307.02892/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 40 references that are in the map.
- First posted on arXiv 6 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech](https://mlsanity.com/paper/1909.07208/) (2019)

## Built on by (most cited first, 1 of 1)

- [Multimodal Domain Generalization for Depression Detection: An Attention-Based BiLSTM Network with Domain-Adversarial Training](https://mlsanity.com/paper/2607.22794/) (2026)

## Abstract (arXiv)

This work shows that depression changes the correlation between features extracted from speech. Furthermore, it shows that using such an insight can improve the training speed and performance of depression detectors based on SVMs and LSTMs. The experiments were performed over the Androids Corpus, a publicly available dataset involving 112 speakers, including 58 people diagnosed with depression by professional psychiatrists. The results show that the models used in the experiments improve in terms of training speed and performance when fed with feature correlation matrices rather than with feature vectors. The relative reduction of the error rate ranges between 23.1% and 26.6% depending on the model. The probable explanation is that feature correlation matrices appear to be more variable in the case of depressed speakers. Correspondingly, such a phenomenon can be thought of as a depression marker.