Papers by Eunjung Yeo
POWSM: A Phonetic Open Whisper-Style Speech Foundation Model (2026.acl-long)
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Chin-Jou Li, Kalvin Chang, Shikhar Bharadwaj, Eunjung Yeo, Kwanghee Choi, Jian Zhu, David R. Mortensen, Shinji Watanabe
| Challenge: | Phone-level modeling of speech is a common approach to speech recognition, but it relies on task-specific architectures and datasets. |
| Approach: | They propose a phonetic framework capable of performing multiple phone-related tasks . they propose 'Phonetic Open Whisper-style Speech Model' that can perform these tasks together . |
| Outcome: | The proposed model outperforms or matches specialized PR models of similar size while supporting G2P, P2G, and ASR. |
[b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic (2026.findings-acl)
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| Challenge: | Existing studies on how self-supervised speech models encode rich phonetic information have not explored how they are structured. |
| Approach: | They conduct a comprehensive analysis of the underlying structure of S3M representations with particular attention to phonological vectors. |
| Outcome: | The proposed model encodes phonologically interpretable and compositional vectors, demonstrating phonology vector arithmetic. |
PRiSM: Benchmarking Phone Realization in Speech Models (2026.acl-long)
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Shikhar Bharadwaj, Chin-Jou Li, Yoonjae Kim, Kwanghee Choi, Eunjung Yeo, Ryan Soh-Eun Shim, Hanyu Zhou, Brendon Boldt, Karen Rosero, Kalvin Chang, Darsh Agrawal, Keer Xu, Chao-Han Huck Yang, Jian Zhu, Shinji Watanabe, David R. Mortensen
| Challenge: | Existing evaluations of phone recognition systems only measure surface-level transcription accuracy. |
| Approach: | They propose to standardize transcription-based evaluation and assess downstream utility in clinical, educational, and multilingual settings with transcription and representation probes. |
| Outcome: | The proposed system outperforms LALMs in clinical, educational, and multilingual settings. |
Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment (2025.naacl-long)
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| Challenge: | Recent phoneme classifiers treat allophonic variation as a single phoneme . atypical pronunciation assessment requires distinguishing between a typical and asymmetric pronunciations . |
| Approach: | They propose a new approach that leverages Gaussian mixture models to model phoneme distributions with multiple subclusters. |
| Outcome: | The proposed approach achieves state-of-the-art across dysarthric and non-native speech datasets. |