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Deep learning techniques have been shown to be efficient in various tasks,especially in the development of speech recognition systems, that is, systemsthat aim to transcribe an audio sentence in a sequence of written words.Despite the progress in the area, speech recognition can still be considereddifficult, especially for languages lacking available data, such as BrazilianPortuguese (BP). In this sense, this work presents the development of an publicAutomatic Speech Recognition (ASR) system using only open available audio data,from the fine-tuning of the Wav2vec 2.0 XLSR-53 model pre-trained in manylanguages, over BP data. The final model presents an average word error rate of12.4% over 7 different datasets (10.5% when applying a language model).According to our knowledge, the obtained error is the lowest among openend-to-end (E2E) ASR models for BP.

Brazilian Portuguese Speech Recognition Using Wav2vec 2.0