Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Electronics

DOI: 10.3390/electronics15071380

Authors: Hana Jang, Taehwa Kim, Hyungwoo Choi, Youngbeom Jung

Institutions: Sungshin Women's University, Pusan National University, Kyungnam University

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion, Division of Human Resource Development