# Empowering Low-Resource Language ASR via Large-Scale Pseudo Labeling

arXiv: https://arxiv.org/abs/2408.14026 · HTML: https://mlsanity.com/paper/2408.14026/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 24 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 21 of 21)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages](https://mlsanity.com/paper/2303.01037/) (2023)
- [Improved Noisy Student Training for Automatic Speech Recognition](https://mlsanity.com/paper/2005.09629/) (2020)
- [SONAR: Sentence-Level Multimodal and Language-Agnostic Representations](https://mlsanity.com/paper/2308.11466/) (2023)
- [Iterative Pseudo-Labeling for Speech Recognition](https://mlsanity.com/paper/2005.09267/) (2020)
- [SlimIPL: Language-Model-Free Iterative Pseudo-Labeling](https://mlsanity.com/paper/2010.11524/) (2020)
- [Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling](https://mlsanity.com/paper/1911.07982/) (2019)
- [Towards Building ASR Systems for the Next Billion Users](https://mlsanity.com/paper/2111.03945/) (2021)
- [IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages](https://mlsanity.com/paper/2403.01926/) (2024)
- [Pseudo-Labeling for Massively Multilingual Speech Recognition](https://mlsanity.com/paper/2111.00161/) (2021)
- [Vistaar: Diverse Benchmarks and Training Sets for Indian Language ASR](https://mlsanity.com/paper/2305.15386/) (2023)
- [Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages](https://mlsanity.com/paper/2208.12666/) (2022)
- [A Study of Transducer based End-to-End ASR with ESPnet: Architecture, Auxiliary Loss and Decoding Strategies](https://mlsanity.com/paper/2201.05420/) (2022)
- [SPRING-INX: A Multilingual Indian Language Speech Corpus by SPRING Lab, IIT Madras](https://mlsanity.com/paper/2310.14654/) (2023)
- [Fast Entropy-Based Methods of Word-Level Confidence Estimation for End-To-End Automatic Speech Recognition](https://mlsanity.com/paper/2212.08703/) (2022)
- [Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition](https://mlsanity.com/paper/2211.04717/) (2022)
- [Iterative Pseudo-Labeling with Deep Feature Annotation and Confidence-Based Sampling](https://mlsanity.com/paper/2109.02717/) (2021)
- [Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition](https://mlsanity.com/paper/2311.03196/) (2023)

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

- [SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition](https://mlsanity.com/paper/2409.10429/) (2024)
- [Methods to Increase the Amount of Data for Speech Recognition for Low Resource Languages](https://mlsanity.com/paper/2501.14788/) (2025)
- [Enabling automatic transcription of child-centered audio recordings from real-world environments](https://mlsanity.com/paper/2506.11747/) (2025)
- [Sometin Beta Pass Notin: Improving Multilingual ASR for Nigerian Languages via Knowledge Distillation](https://mlsanity.com/paper/2605.17710/) (2026)
- [Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR](https://mlsanity.com/paper/2610.13807/) (2026)

## Abstract (arXiv)

In this study, we tackle the challenge of limited labeled data for low-resource languages in ASR, focusing on Hindi. Specifically, we explore pseudo-labeling, by proposing a generic framework combining multiple ideas from existing works. Our framework integrates multiple base models for transcription and evaluators for assessing audio-transcript pairs, resulting in robust pseudo-labeling for low resource languages. We validate our approach with a new benchmark, IndicYT, comprising diverse YouTube audio files from multiple content categories. Our findings show that augmenting pseudo labeled data from YouTube with existing training data leads to significant performance improvements on IndicYT, without affecting performance on out-of-domain benchmarks, demonstrating the efficacy of pseudo-labeled data in enhancing ASR capabilities for low-resource languages. The benchmark, code and models developed as a part of this work will be made publicly available.