# CORAA: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian Portuguese

arXiv: https://arxiv.org/abs/2110.15731 · HTML: https://mlsanity.com/paper/2110.15731/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [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)
- [TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation](https://mlsanity.com/paper/1805.04699/) (2018)
- [The Multilingual TEDx Corpus for Speech Recognition and Translation](https://mlsanity.com/paper/2102.01757/) (2021)
- [CoVoST 2 and Massively Multilingual Speech-to-Text Translation](https://mlsanity.com/paper/2007.10310/) (2020)
- [CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus](https://mlsanity.com/paper/2002.01320/) (2020)
- [MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible](https://mlsanity.com/paper/1907.12895/) (2019)
- [Brazilian Portuguese Speech Recognition Using Wav2vec 2.0](https://mlsanity.com/paper/2107.11414/) (2021)
- [WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm](https://mlsanity.com/paper/2101.05478/) (2021)
- [End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning](https://mlsanity.com/paper/2107.05382/) (2021)

## Built on by (most cited first, 6 of 6)

- [Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 Challenge](https://mlsanity.com/paper/2207.14418/) (2022)
- [Carolina: a General Corpus of Contemporary Brazilian Portuguese with Provenance, Typology and Versioning Information](https://mlsanity.com/paper/2303.16098/) (2023)
- [Toward Conversational Hungarian Speech Recognition: Introducing the BEA-Large and BEA-Dialogue Datasets](https://mlsanity.com/paper/2511.13529/) (2025)
- [Bringing NURC/SP to Digital Life: the Role of Open-source Automatic Speech Recognition Models](https://mlsanity.com/paper/2210.07852/) (2022)
- [Acoustic models of Brazilian Portuguese Speech based on Neural Transformers](https://mlsanity.com/paper/2312.09265/) (2023)
- [Phonetically rich corpus construction for a low-resourced language](https://mlsanity.com/paper/2402.05794/) (2024)

## Abstract (arXiv)

Automatic Speech recognition (ASR) is a complex and challenging task. In recent years, there have been significant advances in the area. In particular, for the Brazilian Portuguese (BP) language, there were about 376 hours public available for ASR task until the second half of 2020. With the release of new datasets in early 2021, this number increased to 574 hours. The existing resources, however, are composed of audios containing only read and prepared speech. There is a lack of datasets including spontaneous speech, which are essential in different ASR applications. This paper presents CORAA (Corpus of Annotated Audios) v1. with 290.77 hours, a publicly available dataset for ASR in BP containing validated pairs (audio-transcription). CORAA also contains European Portuguese audios (4.69 hours). We also present a public ASR model based on Wav2Vec 2.0 XLSR-53 and fine-tuned over CORAA. Our model achieved a Word Error Rate of 24.18% on CORAA test set and 20.08% on Common Voice test set. When measuring the Character Error Rate, we obtained 11.02% and 6.34% for CORAA and Common Voice, respectively. CORAA corpora were assembled to both improve ASR models in BP with phenomena from spontaneous speech and motivate young researchers to start their studies on ASR for Portuguese. All the corpora are publicly available at https://github.com/nilc-nlp/CORAA under the CC BY-NC-ND 4.0 license.