# Bridging the Language Gap: Synthetic Voice Diversity via Latent Mixup for Equitable Speech Recognition

arXiv: https://arxiv.org/abs/2511.20534 · HTML: https://mlsanity.com/paper/2511.20534/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 8 of 8)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Diff-HierVC: Diffusion-based Hierarchical Voice Conversion with Robust Pitch Generation and Masked Prior for Zero-shot Speaker Adaptation](https://mlsanity.com/paper/2311.04693/) (2023)
- [Latent Filling: Latent Space Data Augmentation for Zero-shot Speech Synthesis](https://mlsanity.com/paper/2310.03538/) (2023)
- [Voice Conversion Augmentation for Speaker Recognition on Defective Datasets](https://mlsanity.com/paper/2404.00863/) (2024)
- [MixRep: Hidden Representation Mixup for Low-Resource Speech Recognition](https://mlsanity.com/paper/2310.18450/) (2023)
- [A Comprehensive Investigation on Speaker Augmentation for Speaker Recognition](https://mlsanity.com/paper/2406.07421/) (2024)

## Abstract (arXiv)

Modern machine learning models for audio tasks often exhibit superior performance on English and other well-resourced languages, primarily due to the abundance of available training data. This disparity leads to an unfair performance gap for low-resource languages, where data collection is both challenging and costly. In this work, we introduce a novel data augmentation technique for speech corpora designed to mitigate this gap. Through comprehensive experiments, we demonstrate that our method significantly improves the performance of automatic speech recognition systems on low-resource languages. Furthermore, we show that our approach outperforms existing augmentation strategies, offering a practical solution for enhancing speech technology in underrepresented linguistic communities.