# Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR

arXiv: https://arxiv.org/abs/2506.04364 · HTML: https://mlsanity.com/paper/2506.04364/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Domain Adversarial Training for Accented Speech Recognition](https://mlsanity.com/paper/1806.02786/) (2018)
- [The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR](https://mlsanity.com/paper/2303.18110/) (2023)
- [GLOBE: A High-quality English Corpus with Global Accents for Zero-shot Speaker Adaptive Text-to-Speech](https://mlsanity.com/paper/2406.14875/) (2024)
- [Voice Conversion Can Improve ASR in Very Low-Resource Settings](https://mlsanity.com/paper/2111.02674/) (2021)
- [Accented Speech Recognition: A Survey](https://mlsanity.com/paper/2104.10747/) (2021)
- [Exploring data augmentation in bias mitigation against non-native-accented speech](https://mlsanity.com/paper/2312.15499/) (2023)
- [Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning](https://mlsanity.com/paper/2110.00678/) (2021)
- [Leveraging native language information for improved accented speech recognition](https://mlsanity.com/paper/1904.09038/) (2019)
- [Accented Speech Recognition With Accent-specific Codebooks](https://mlsanity.com/paper/2310.15970/) (2023)
- [The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese](https://mlsanity.com/paper/2402.07513/) (2024)
- [More Speaking or More Speakers?](https://mlsanity.com/paper/2211.00854/) (2022)
- [Synthetic Cross-accent Data Augmentation for Automatic Speech Recognition](https://mlsanity.com/paper/2303.00802/) (2023)

## Built on by (most cited first, 2 of 2)

- [Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West Frisian](https://mlsanity.com/paper/2605.19711/) (2026)
- [ReCo: When to Relocate Sensor Kits under Deployment Constraints -- A NILM Case Study](https://mlsanity.com/paper/2609.35075/) (2026)

## Abstract (arXiv)

To build an automatic speech recognition (ASR) system that can serve everyone in the world, the ASR needs to be robust to a wide range of accents including unseen accents. We systematically study how three different variables in training data -- the number of speakers, the audio duration per each individual speaker, and the diversity of accents -- affect ASR robustness towards unseen accents in a low-resource training regime. We observe that for a fixed number of ASR training hours, it is more beneficial to increase the number of speakers (which means each speaker contributes less) than the number of hours contributed per speaker. We also observe that more speakers enables ASR performance gains from scaling number of hours. Surprisingly, we observe minimal benefits to prioritizing speakers with different accents when the number of speakers is controlled. Our work suggests that practitioners should prioritize increasing the speaker count in ASR training data composition for new languages.