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

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