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

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.

Download paper here

Download dataset here

Bibtex:

@article{candido2022coraa, title={CORAA ASR: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian Portuguese}, author={Candido Junior, Arnaldo and Casanova, Edresson and Soares, Anderson and de Oliveira, Frederico Santos and Oliveira, Lucas and Junior, Ricardo Corso Fernandes and da Silva, Daniel Peixoto Pinto and Fayet, Fernando Gorgulho and Carlotto, Bruno Baldissera and Gris, Lucas Rafael Stefanel and others}, journal={Language Resources and Evaluation}, pages={1–33}, year={2022}, publisher={Springer} }