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The Impact of Data Augmentation Techniques on Improving Speech Recognition Performance for English in Indonesian Children Based on Wav2Vec 2.0

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 2 (2025) · pp. 165–176

DOI
10.22146/ijccs.104646
ISSN
2460-7258, 1978-1520
Published
2025-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Early childhood education is a crucial phase in shaping children's character and language skills. This study develops an Automatic Speech Recognition (ASR) model to recognize the speech of Indonesian children speaking English. The process begins with collecting and processing a dataset of children's speech recordings, which is then expanded using data augmentation techniques to enhance pronunciation variations. The pre-trained ASR Wav2Vec 2.0 model is fine-tuned with both the original and augmented datasets. Evaluation using Word Error Rate (WER) and Character Error Rate (CER) shows a significant accuracy improvement, with WER decreasing from 53% to 45% and CER from 33% to 27%, reflecting a performance increase of approximately 15%. Further analysis reveals pronunciation errors in phonemes such as /ð/, /θ/, /r/, /v/, /z/, and /ʃ/, which are uncommon in the Indonesian language, manifesting as substitutions, omissions, or additions in words like "three," "that," "rabbit," "very," and "zebra." These findings highlight the need for targeted phoneme training, audio-based approaches with ASR feedback, and the listen-and-repeat technique in English language instruction for children.Keywords— Early childhood education, Automatic Speech Recognition, Augmentation, Character Error Rate, Word Error Rate

Keywords

Collaboration network of the authors

3 authors · 2 co-authorship links

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  • Binus University
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Co-authorOrganisationJoint articles
Hendra KurniawanBinus University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">The Impact of Data Augmentation Techniques on Improving Speech Recognition Performance for English in Indonesian Children Based on Wav2Vec 2.0</dc:title>
	<dc:creator>Maskur, Maimunah</dc:creator>
	<dc:creator>Zahra, Amalia</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Automatic Speech Recognition, Augmentation, Character Error Rate, Word Error Rate</dc:subject>
	<dc:description xml:lang="en-US">Early childhood education is a crucial phase in shaping children's character and language skills. This study develops an Automatic Speech Recognition (ASR) model to recognize the speech of Indonesian children speaking English. The process begins with collecting and processing a dataset of children's speech recordings, which is then expanded using data augmentation techniques to enhance pronunciation variations. The pre-trained ASR Wav2Vec 2.0 model is fine-tuned with both the original and augmented datasets. Evaluation using Word Error Rate (WER) and Character Error Rate (CER) shows a significant accuracy improvement, with WER decreasing from 53% to 45% and CER from 33% to 27%, reflecting a performance increase of approximately 15%. Further analysis reveals pronunciation errors in phonemes such as /ð/, /θ/, /r/, /v/, /z/, and /ʃ/, which are uncommon in the Indonesian language, manifesting as substitutions, omissions, or additions in words like "three," "that," "rabbit," "very," and "zebra." These findings highlight the need for targeted phoneme training, audio-based approaches with ASR feedback, and the listen-and-repeat technique in English language instruction for children.Keywords— Early childhood education, Automatic Speech Recognition, Augmentation, Character Error Rate, Word Error Rate</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/104646</dc:identifier>
	<dc:identifier>10.22146/ijccs.104646</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 2 (2025): April; 165-176</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/104646/41526</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>