Academic Journal
Enhancing Korean-Accented English ASR with Transliteration-Based Data Synthesis.
| Τίτλος: | Enhancing Korean-Accented English ASR with Transliteration-Based Data Synthesis. |
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| Συγγραφείς: | Jang, Hana, Kim, Taehwa, Choi, Hyungwoo, Jung, Youngbeom |
| Πηγή: | Electronics (2079-9292); Apr2026, Vol. 15 Issue 7, p1380, 23p |
| Θεματικοί όροι: | Transliteration, Data augmentation, Automatic speech recognition, Error rates, Korean language, Phonetic transcriptions |
| Περίληψη: | Despite recent advances in automatic speech recognition (ASR), performance remains limited for Korean-accented English due to the limited availability of accent-specific speech data, including pronunciation and prosodic variations. To address this limitation, we propose a synthetic data generation framework for improving Whisper-based ASR performance. Synthetic speech is generated by converting English text into Hangul-based phonetic transcriptions using an intermediate IPA representation to reflect the phonological characteristics of Korean-accented English. The ASR model is fine-tuned using Low-Rank Adaptation with a mixture of synthetic and authentic speech data. Experimental results demonstrate relative reductions of up to 16.40% in the character error rate, 14.93% in the word error rate, and 14.81% in the phoneme error rate compared to the pretrained baseline. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20799292&ISBN=&volume=15&issue=7&date=20260401&spage=1380&pages=1380-1402&title=Electronics (2079-9292)&atitle=Enhancing%20Korean-Accented%20English%20ASR%20with%20Transliteration-Based%20Data%20Synthesis.&aulast=Jang%2C%20Hana&id=DOI:10.3390/electronics15071380 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancing Korean-Accented English ASR with Transliteration-Based Data Synthesis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jang%2C+Hana%22">Jang, Hana</searchLink><br /><searchLink fieldCode="AR" term="%22Kim%2C+Taehwa%22">Kim, Taehwa</searchLink><br /><searchLink fieldCode="AR" term="%22Choi%2C+Hyungwoo%22">Choi, Hyungwoo</searchLink><br /><searchLink fieldCode="AR" term="%22Jung%2C+Youngbeom%22">Jung, Youngbeom</searchLink> – Name: TitleSource Label: Source Group: Src Data: Electronics (2079-9292); Apr2026, Vol. 15 Issue 7, p1380, 23p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Transliteration%22">Transliteration</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Error+rates%22">Error rates</searchLink><br /><searchLink fieldCode="DE" term="%22Korean+language%22">Korean language</searchLink><br /><searchLink fieldCode="DE" term="%22Phonetic+transcriptions%22">Phonetic transcriptions</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Despite recent advances in automatic speech recognition (ASR), performance remains limited for Korean-accented English due to the limited availability of accent-specific speech data, including pronunciation and prosodic variations. To address this limitation, we propose a synthetic data generation framework for improving Whisper-based ASR performance. Synthetic speech is generated by converting English text into Hangul-based phonetic transcriptions using an intermediate IPA representation to reflect the phonological characteristics of Korean-accented English. The ASR model is fine-tuned using Low-Rank Adaptation with a mixture of synthetic and authentic speech data. Experimental results demonstrate relative reductions of up to 16.40% in the character error rate, 14.93% in the word error rate, and 14.81% in the phoneme error rate compared to the pretrained baseline. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/electronics15071380 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1380 Subjects: – SubjectFull: Transliteration Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Automatic speech recognition Type: general – SubjectFull: Error rates Type: general – SubjectFull: Korean language Type: general – SubjectFull: Phonetic transcriptions Type: general Titles: – TitleFull: Enhancing Korean-Accented English ASR with Transliteration-Based Data Synthesis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jang, Hana – PersonEntity: Name: NameFull: Kim, Taehwa – PersonEntity: Name: NameFull: Choi, Hyungwoo – PersonEntity: Name: NameFull: Jung, Youngbeom IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20799292 Numbering: – Type: volume Value: 15 – Type: issue Value: 7 Titles: – TitleFull: Electronics (2079-9292) Type: main |
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