# A Novel Self-training Approach for Low-resource Speech Recognition

arXiv: https://arxiv.org/abs/2308.05269 · HTML: https://mlsanity.com/paper/2308.05269/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Revisiting Self-Training for Neural Sequence Generation](https://mlsanity.com/paper/1909.13788/) (2019)
- [Improved Noisy Student Training for Automatic Speech Recognition](https://mlsanity.com/paper/2005.09629/) (2020)
- [Self-Training for End-to-End Speech Recognition](https://mlsanity.com/paper/1909.09116/) (2019)
- [An analysis of incorporating an external language model into a sequence-to-sequence model](https://mlsanity.com/paper/1712.01996/) (2017)
- [Self-training and Pre-training are Complementary for Speech Recognition](https://mlsanity.com/paper/2010.11430/) (2020)
- [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)
- [Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition](https://mlsanity.com/paper/2106.08922/) (2021)
- [Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training](https://mlsanity.com/paper/2011.13439/) (2020)
- [Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages](https://mlsanity.com/paper/2012.12121/) (2020)
- [Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages](https://mlsanity.com/paper/1806.05059/) (2018)
- [Pseudo-Labeling for Massively Multilingual Speech Recognition](https://mlsanity.com/paper/2111.00161/) (2021)
- [Semi-Supervised Speech Recognition via Graph-based Temporal Classification](https://mlsanity.com/paper/2010.15653/) (2020)
- [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)
- [XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition](https://mlsanity.com/paper/2103.08207/) (2021)
- [Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy](https://mlsanity.com/paper/2110.04948/) (2021)
- [Improved Meta Learning for Low Resource Speech Recognition](https://mlsanity.com/paper/2205.06182/) (2022)
- [Filter and evolve: progressive pseudo label refining for semi-supervised automatic speech recognition](https://mlsanity.com/paper/2210.16318/) (2022)

## Built on by (most cited first, 7 of 7)

- [How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario](https://mlsanity.com/paper/2411.18217/) (2024)
- [The Greek podcast corpus: Competitive speech models for low-resourced languages with weakly supervised data](https://mlsanity.com/paper/2406.15284/) (2024)
- [SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition](https://mlsanity.com/paper/2409.10429/) (2024)
- [PhasePerturbation: Speech Data Augmentation via Phase Perturbation for Automatic Speech Recognition](https://mlsanity.com/paper/2312.08571/) (2023)
- [ManWav: The First Manchu ASR Model](https://mlsanity.com/paper/2406.13502/) (2024)
- [Robust Cross-Etiology and Speaker-Independent Dysarthric Speech Recognition](https://mlsanity.com/paper/2501.14994/) (2025)
- [Weak Supervision Techniques towards Enhanced ASR Models in Industry-level CRM Systems](https://mlsanity.com/paper/2507.16843/) (2025)

## Abstract (arXiv)

In this paper, we propose a self-training approach for automatic speech recognition (ASR) for low-resource settings. While self-training approaches have been extensively developed and evaluated for high-resource languages such as English, their applications to low-resource languages like Punjabi have been limited, despite the language being spoken by millions globally. The scarcity of annotated data has hindered the development of accurate ASR systems, especially for low-resource languages (e.g., Punjabi and M=aori languages). To address this issue, we propose an effective self-training approach that generates highly accurate pseudo-labels for unlabeled low-resource speech. Our experimental analysis demonstrates that our approach significantly improves word error rate, achieving a relative improvement of 14.94% compared to a baseline model across four real speech datasets. Further, our proposed approach reports the best results on the Common Voice Punjabi dataset.