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Speech recognition datasets for low-resource Congolese languages.

Ussen Kimanuka1, Ciira Wa Maina2,3, Osman Büyük4

  • 1Department of Electrical Engineering, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya.

Data in Brief
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

New speech recognition datasets for Lingala and other Congolese languages were created. These resources aid in developing advanced Automatic Speech Recognition (ASR) models for low-resource languages.

Keywords:
Automatic speech recognitionCross-lingual acoustic modelMultilingual acoustic modelPre-trained modelsSelf-supervised learningTransfer learning

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Area of Science:

  • Computational Linguistics
  • Speech Processing
  • Low-Resource Language Technologies

Background:

  • Large pre-trained Automatic Speech Recognition (ASR) models benefit from transfer learning, but require substantial data.
  • Many low-resource languages lack sufficient data to fully leverage transfer learning.
  • Benchmark corpora are essential for advancing ASR methods in data-scarce linguistic contexts.

Purpose of the Study:

  • To introduce two novel benchmark corpora for low-resource languages in the Democratic Republic of the Congo.
  • To facilitate the development of monolingual and multilingual ASR systems for Congolese languages.
  • To enable inaugural benchmarking of speech recognition systems for Lingala and four other Congolese languages.

Main Methods:

  • Creation of the Lingala Read Speech Corpus (4h labeled audio) with diverse speakers and accents.
  • Compilation of the Congolese Speech Radio Corpus (741h unlabeled audio) from broadcast archives.
  • Application of supervised learning and self-supervised learning techniques for model development and benchmarking.

Main Results:

  • The developed corpora provide valuable resources for ASR research in low-resource settings.
  • Successful inaugural benchmarking of speech recognition systems for Lingala.
  • Development of the first multilingual ASR model for four Congolese languages, serving 95 million people.

Conclusions:

  • The released datasets are crucial for advancing ASR research and development for underserved languages.
  • These resources pave the way for improved speech recognition technologies in the Democratic Republic of the Congo.
  • The study highlights the potential of transfer learning and novel corpora for low-resource ASR.