Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning

Eduardo Medeiros (), Leonel Corado, Luís Rato (), Paulo Quaresma and Pedro Salgueiro
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Eduardo Medeiros: Escola de Ciências e Tecnologia, Universidade de Évora, 7000-671 Évora, Portugal
Leonel Corado: Escola de Ciências e Tecnologia, Universidade de Évora, 7000-671 Évora, Portugal
Luís Rato: Escola de Ciências e Tecnologia, Universidade de Évora, 7000-671 Évora, Portugal
Paulo Quaresma: Escola de Ciências e Tecnologia, Universidade de Évora, 7000-671 Évora, Portugal
Pedro Salgueiro: Escola de Ciências e Tecnologia, Universidade de Évora, 7000-671 Évora, Portugal

Future Internet, 2023, vol. 15, issue 5, 1-16

Abstract: Automatic speech recognition (ASR), commonly known as speech-to-text, is the process of transcribing audio recordings into text, i.e., transforming speech into the respective sequence of words. This paper presents a deep learning ASR system optimization and evaluation for the European Portuguese language. We present a pipeline composed of several stages for data acquisition, analysis, pre-processing, model creation, and evaluation. A transfer learning approach is proposed considering an English language-optimized model as starting point; a target composed of European Portuguese; and the contribution to the transfer process by a source from a different domain consisting of a multiple-variant Portuguese language dataset, essentially composed of Brazilian Portuguese. A domain adaptation was investigated between European Portuguese and mixed (mostly Brazilian) Portuguese. The proposed optimization evaluation used the NVIDIA NeMo framework implementing the QuartzNet15×5 architecture based on 1D time-channel separable convolutions. Following this transfer learning data-centric approach, the model was optimized, achieving a state-of-the-art word error rate (WER) of 0.0503.

Keywords: machine learning; deep learning; deep neural networks; speech-to-text; automatic speech recognition; NVIDIA NeMo; GPUs; data-centric; Portuguese language (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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