# FrAUG: A Frame Rate Based Data Augmentation Method for Depression Detection from Speech Signals

arXiv: https://arxiv.org/abs/2202.05912 · HTML: https://mlsanity.com/paper/2202.05912/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2022.

## Built on by (most cited first, 5 of 5)

- [A knowledge-driven vowel-based approach of depression classification from speech using data augmentation](https://mlsanity.com/paper/2210.15261/) (2022)
- [Attention-Based Acoustic Feature Fusion Network for Depression Detection](https://mlsanity.com/paper/2308.12478/) (2023)
- [RBA-FE: A Robust Brain-Inspired Audio Feature Extractor for Depression Diagnosis](https://mlsanity.com/paper/2506.07118/) (2025)
- [Enhancing Video-Based Robot Failure Detection Using Task Knowledge](https://mlsanity.com/paper/2508.18705/) (2025)
- [DepFlow: Disentangled Speech Generation to Mitigate Semantic Bias in Depression Detection](https://mlsanity.com/paper/2601.00303/) (2026)

## Abstract (arXiv)

In this paper, a data augmentation method is proposed for depression detection from speech signals. Samples for data augmentation were created by changing the frame-width and the frame-shift parameters during the feature extraction process. Unlike other data augmentation methods (such as VTLP, pitch perturbation, or speed perturbation), the proposed method does not explicitly change acoustic parameters but rather the time-frequency resolution of frame-level features. The proposed method was evaluated using two different datasets, models, and input acoustic features. For the DAIC-WOZ (English) dataset when using the DepAudioNet model and mel-Spectrograms as input, the proposed method resulted in an improvement of 5.97% (validation) and 25.13% (test) when compared to the baseline. The improvements for the CONVERGE (Mandarin) dataset when using the x-vector embeddings with CNN as the backend and MFCCs as input features were 9.32% (validation) and 12.99% (test). Baseline systems do not incorporate any data augmentation. Further, the proposed method outperformed commonly used data-augmentation methods such as noise augmentation, VTLP, Speed, and Pitch Perturbation. All improvements were statistically significant.