# Exploring data augmentation in bias mitigation against non-native-accented speech

arXiv: https://arxiv.org/abs/2312.15499 · HTML: https://mlsanity.com/paper/2312.15499/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2023.

## Built on by (most cited first, 11 of 11)

- [A Contrastive Learning Approach to Mitigate Bias in Speech Models](https://mlsanity.com/paper/2406.14686/) (2024)
- [Improving child speech recognition with augmented child-like speech](https://mlsanity.com/paper/2406.10284/) (2024)
- [Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource Languages](https://mlsanity.com/paper/2505.14874/) (2025)
- [EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition](https://mlsanity.com/paper/2506.04652/) (2025)
- [How to Evaluate Automatic Speech Recognition: Comparing Different Performance and Bias Measures](https://mlsanity.com/paper/2507.05885/) (2025)
- [Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR](https://mlsanity.com/paper/2506.04364/) (2025)
- [CO-VADA: A Confidence-Oriented Voice Augmentation Debiasing Approach for Fair Speech Emotion Recognition](https://mlsanity.com/paper/2506.06071/) (2025)
- [Fairness in Dysarthric Speech Synthesis: Understanding Intrinsic Bias in Dysarthric Speech Cloning using F5-TTS](https://mlsanity.com/paper/2508.05102/) (2025)
- ["OK Aura, Be Fair With Me": Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detection](https://mlsanity.com/paper/2604.05830/) (2026)
- [Evaluating Bias in Phoneme-Based Automatic Speech Recognition Systems: An Analysis of IPA Transcription Models](https://mlsanity.com/paper/2606.11639/) (2026)
- [Comparing Human and Automatic Recognition of Dutch Dysarthric Continuous Speech: A Case Study](https://mlsanity.com/paper/2606.30237/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) should serve every speaker, not only the majority ``standard'' speakers of a language. In order to build inclusive ASR, mitigating the bias against speaker groups who speak in a ``non-standard'' or ``diverse'' way is crucial. We aim to mitigate the bias against non-native-accented Flemish in a Flemish ASR system. Since this is a low-resource problem, we investigate the optimal type of data augmentation, i.e., speed/pitch perturbation, cross-lingual voice conversion-based methods, and SpecAugment, applied to both native Flemish and non-native-accented Flemish, for bias mitigation. The results showed that specific types of data augmentation applied to both native and non-native-accented speech improve non-native-accented ASR while applying data augmentation to the non-native-accented speech is more conducive to bias reduction. Combining both gave the largest bias reduction for human-machine interaction (HMI) as well as read-type speech.