Automatic speech recognition (ASR) performs well for high-resource languages with abundant paired audio-transcript data, but its accuracy degrades sharply for most languages due to limited publicly available aligned data. To this end, we introduce WorldSpeech, a 24 kHz multilingual speech corpus comprising 65k hours of aligned audio-transcript data across 76 languages, collected from diverse public sources including parliamentary proceedings, international broadcasts, and public-domain audiobooks. For 37 languages, WorldSpeech provides more than 200 hours of aligned speech, with 28 exceeding 500 hours and 24 surpassing 1k hours. Fine-tuning existing ASR models on WorldSpeech results in an average relative Word-Error-Rate reduction of 63.5% across 11 typologically diverse languages.
@article{arxiv.2605.09167,
title = {WorldSpeech: A Multilingual Speech Corpus from Around the World},
author = {Antonis Asonitis and Luca A. Lanzendörfer and Frédéric Berdoz and Roger Wattenhofer},
journal= {arXiv preprint arXiv:2605.09167},
year = {2026}
}