FalAR: A Large-scale Speaker-Annotated European Portuguese Speech Corpus of Parliamentary Sessions
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Computer Science > Computation and Language
arXiv:2605.27062 (cs)
[Submitted on 26 May 2026]
Title:FalAR: A Large-scale Speaker-Annotated European Portuguese Speech Corpus of Parliamentary Sessions
Authors:Francisco Teixeira, Carlos Carvalho, Mariana Julião, Catarina Botelho, Rubén Solera-Ureña, Sérgio Paulo, Thomas Rolland, Ben Peters, Isabel Trancoso, Alberto Abad
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Abstract:State-of-the-art performance for Automatic Speech Recognition (ASR) largely depends on the availability of large-scale labeled corpora. This creates a demand for increased data collection efforts, particularly for under-represented languages and dialectal varieties. Due to having considerably fewer speakers (around 11 million), European Portuguese (EP) is overshadowed by Brazilian Portuguese (BP) (around 200 million speakers) in currently available large-scale speech data resources, resulting in under-performing speech-based systems for EP users. To address this gap, and following similar data collection efforts for other languages, we present FalAR, a large-scale, speaker-annotated speech corpus of European Portuguese parliamentary sessions. Spanning approximately 20 years, FalAR comprises 5,800 hours of speech data. In addition, 4,850 hours have speaker identity annotations, for a total of 1,180 speakers with associated metadata including age, gender, political affiliation, and parliamentary role. The corpus was built using a state-of-the-art EP CAMÕES ASR model for transcription-reference alignment. In this paper, we describe the data collection process, together with the main characteristics of the FalAR corpus. Furthermore, we evaluate the trade-off between data quantity and alignment accuracy on ASR performance, with our experiments demonstrating that incorporating FalAR as pre-training data yields up to 14% relative WER improvement over baseline models.
| Comments: | Published in LREC2026 |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.27062 [cs.CL] |
| (or arXiv:2605.27062v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27062
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Francisco Sepúlveda Teixeira [view email][v1] Tue, 26 May 2026 14:14:37 UTC (381 KB)
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