# Unified Semi-Supervised Pipeline for Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2506.07659 · HTML: https://mlsanity.com/paper/2506.07659/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2025.

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

- [ReHear: Iterative Pseudo-Label Refinement for Semi-Supervised Speech Recognition via Audio Large Language Models](https://mlsanity.com/paper/2602.18721/) (2026)
- [StreamHear: Domain-Adapted Pseudo-Labeling for Semi-Supervised Streaming Speech Recognition](https://mlsanity.com/paper/2608.13717/) (2026)

## Abstract (arXiv)

Automatic Speech Recognition has been a longstanding research area, with substantial efforts dedicated to integrating semi-supervised learning due to the scarcity of labeled datasets. However, most prior work has focused on improving learning algorithms using existing datasets, without providing a complete public framework for large-scale semi-supervised training across new datasets or languages. In this work, we introduce a fully open-source semi-supervised training framework encompassing the entire pipeline: from unlabeled data collection to pseudo-labeling and model training. Our approach enables scalable dataset creation for any language using publicly available speech data under Creative Commons licenses. We also propose a novel pseudo-labeling algorithm, TopIPL, and evaluate it in both low-resource (Portuguese, Armenian) and high-resource (Spanish) settings. Notably, TopIPL achieves relative WER improvements of 18-40% for Portuguese, 5-16% for Armenian, and 2-8% for Spanish.