Papers by Ryan Soh-Eun Shim

4 papers
Linear Script Representations in Speech Foundation Models Enable Zero-Shot Transliteration (2026.findings-acl)

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Challenge: We show that script information is linearly encoded in the activation space of multilingual speech models . modifying activations at inference time induces script change even in unconventional pairings .
Approach: They propose to add script vectors to activations at test time to induce script change . they also show that script information is linearly encoded in the activation space of multilingual speech models .
Outcome: The proposed approach can induce script change even in unconventional language-script pairings.
Phonotactic Complexity across Dialects (2024.lrec-main)

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Challenge: Recent studies show a moderate negative correlation between phonotactic complexity and word length in 106 languages.
Approach: They propose to use a phone-level language model to measure phonotactic complexity . they find a tradeoff between word length and phonomactic complex .
Outcome: The proposed model shows that low phonotactic complexity dialects concentrate around capital regions.
PRiSM: Benchmarking Phone Realization in Speech Models (2026.acl-long)

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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.
Dialetto, ma Quanto Dialetto? Transcribing and Evaluating Dialects on a Continuum (2025.findings-naacl)

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Challenge: Recent work on dialect variation in NLP treats dialects as discrete categories . dialect variation is a focus of increasing interest in the field .
Approach: They examine performance differences between Italian dialects by incorporating performance data from different regions of the world.
Outcome: The results show that performance disparities are due to dialects that are more similar to the standard variety.

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