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End-to-end indonesian speech recognition with convolutional and gated recurrent units

Journal of physics. Conference series, 2020-06, Vol.1566 (1), p.12118 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1566/1/012118

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  • Title:
    End-to-end indonesian speech recognition with convolutional and gated recurrent units
  • Author: Adiwidjaja, Rifqi ; Ivan Fanany, M
  • Subjects: Automatic speech recognition ; Deep learning ; Indonesian language ; Machine learning ; Physics ; Speech recognition ; Spoken language ; Voice recognition
  • Is Part Of: Journal of physics. Conference series, 2020-06, Vol.1566 (1), p.12118
  • Description: Automatic Speech Recognition has penetrated deeply into our life. For well-resourced language, it can be considered as solved, but that's not the case for under-resourced language like Bahasa. Although it's the 7th most spoken language in the world, the research of speech recognition for Bahasa was still extremely limited, with setting still inconvenient for the real world and industry. This research is an attempt to make a speech recognition model that has applicability to the real world and industry, specifically that supports sentence level input with variable character length with end-to-end training. We built the model using the deep learning approach, specifically utilizing the residual networks and Bi-Directional Gated Recurrent Unit (Bi-GRU). To the best of our knowledge, this is the first Indonesian ASR model that can be trained in an end-to-end manner. Our model surpassed the baseline model on all metrics and achieve competitiveness with the current best result, which used the visual modal, for the dataset even with a more difficult and prone to noise modality like sound.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1566/1/012118
  • Source: IOP Publishing
    Geneva Foundation Free Medical Journals at publisher websites
    IOPscience (Open Access)
    ProQuest Databases
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