Unified Semi-Supervised Pipeline for Automatic Speech Recognition
Abstract
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.
Keywords
Cite
@article{arxiv.2506.07659,
title = {Unified Semi-Supervised Pipeline for Automatic Speech Recognition},
author = {Nune Tadevosyan and Nikolay Karpov and Andrei Andrusenko and Vitaly Lavrukhin and Ante Jukic},
journal= {arXiv preprint arXiv:2506.07659},
year = {2025}
}
Comments
Accepted to Interspeech 2025