Brazilian Portuguese Speech Recognition Using Wav2vec 2.0

Deep learning techniques have been shown to be efficient in various tasks, especially in the development of speech recognition systems, that is, systems that aim to transcribe an audio sentence in a sequence of written words. Despite the progress in the area, speech recognition can still be consider...

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Bibliographic Details
Published in:arXiv.org 2021-12
Main Authors: Lucas Rafael Stefanel Gris, Casanova, Edresson, Santos de Oliveira, Frederico, Anderson da Silva Soares, Arnaldo Candido Junior
Format: Article
Language:English
Subjects:
Audio data
Automatic speech recognition
Deep learning
Languages
Machine learning
Voice recognition
Online Access:Get full text
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Summary:Deep learning techniques have been shown to be efficient in various tasks, especially in the development of speech recognition systems, that is, systems that aim to transcribe an audio sentence in a sequence of written words. Despite the progress in the area, speech recognition can still be considered difficult, especially for languages lacking available data, such as Brazilian Portuguese (BP). In this sense, this work presents the development of an public Automatic 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 many languages, over BP data. The final model presents an average word error rate of 12.4% over 7 different datasets (10.5% when applying a language model). According to our knowledge, the obtained error is the lowest among open end-to-end (E2E) ASR models for BP.
ISSN:2331-8422