# EuroSpeech: A Multilingual Speech Corpus

arXiv: https://arxiv.org/abs/2510.00514 · HTML: https://mlsanity.com/paper/2510.00514/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 20 of 20)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages](https://mlsanity.com/paper/2303.01037/) (2023)
- [Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation](https://mlsanity.com/paper/2407.05361/) (2024)
- [Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context](https://mlsanity.com/paper/2309.08105/) (2023)
- [YODAS: Youtube-Oriented Dataset for Audio and Speech](https://mlsanity.com/paper/2406.00899/) (2024)
- [GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement](https://mlsanity.com/paper/2406.11546/) (2024)
- [MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages](https://mlsanity.com/paper/2410.01036/) (2024)
- [MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of Transcribed Audio for Speech Recognition Research](https://mlsanity.com/paper/2406.18301/) (2024)
- [FT Speech: Danish Parliament Speech Corpus](https://mlsanity.com/paper/2005.12368/) (2020)
- [The Norwegian Parliamentary Speech Corpus](https://mlsanity.com/paper/2201.10881/) (2022)
- [Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach](https://mlsanity.com/paper/2406.01446/) (2024)
- [The ParlaSpeech Collection of Automatically Generated Speech and Text Datasets from Parliamentary Proceedings](https://mlsanity.com/paper/2409.15397/) (2024)
- [MASRI-HEADSET: A Maltese Corpus for Speech Recognition](https://mlsanity.com/paper/2008.05760/) (2020)
- [Finnish Parliament ASR corpus - Analysis, benchmarks and statistics](https://mlsanity.com/paper/2203.14876/) (2022)

## Built on by (most cited first, 4 of 4)

- [OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models](https://mlsanity.com/paper/2604.00688/) (2026)
- [WorldSpeech: A Multilingual Speech Corpus from Around the World](https://mlsanity.com/paper/2605.09167/) (2026)
- [STREAM: A Data-Centric Framework for Mining High-Value Task-Oriented Dialogues from Streaming Media](https://mlsanity.com/paper/2605.25162/) (2026)
- [Scaling Forced Alignment to End-User Devices](https://mlsanity.com/paper/2609.21145/) (2026)

## Abstract (arXiv)

Recent progress in speech processing has highlighted that high-quality performance across languages requires substantial training data for each individual language. While existing multilingual datasets cover many languages, they often contain insufficient data for most languages. Thus, trained models perform poorly on the majority of the supported languages. Our work addresses this challenge by introducing a scalable pipeline for constructing speech datasets from parliamentary recordings. The proposed pipeline includes robust components for media retrieval and a two-stage alignment algorithm designed to handle non-verbatim transcripts and long-form audio. Applying this pipeline to recordings from 22 European parliaments, we extract over 61k hours of aligned speech segments, achieving substantial per-language coverage with 19 languages exceeding 1k hours and 22 languages exceeding 500 hours of high-quality speech data. We obtain an average 41.8% reduction in word error rates over baselines when finetuning an existing ASR model on our dataset, demonstrating the usefulness of our approach.