Academic Journal

Enhancing Korean-Accented English ASR with Transliteration-Based Data Synthesis.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Enhancing Korean-Accented English ASR with Transliteration-Based Data Synthesis.
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
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  Label: Title
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  Data: Enhancing Korean-Accented English ASR with Transliteration-Based Data Synthesis.
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  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>
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  Data: Electronics (2079-9292); Apr2026, Vol. 15 Issue 7, p1380, 23p
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  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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        Value: 10.3390/electronics15071380
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      – Code: eng
        Text: English
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      – 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
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            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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