  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.
