Author

Sangkwon Park

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Preprint Oct 2026

SSEmb: Training a Generalizable Speech Retrieval Model Using Only Automatic Speech Recognition Data

End-to-end speech-to-text retrieval is becoming an important capability for voice agents, allowing spoken queries to retrieve relevant documents without intermediate transcription. Training these retrievers typically depends on large collections of spoken query-document pairs that are costly to acquire and difficult to...

Sangkwon Park, Dongwook Lee, Younghoon Cho et al. · 0 citations

#natural language process... Preprint Sep 2026

Phrase-Localized Language-Contrastive Guidance: Training-Free Localized Accent Control for Code-Switching Text-to-Speech

Phrase-Localized Language-Contrastive Guidance (LCG), a training-free inference framework that restores a native accent to code-switched phrases in cross-lingual text-to-speech, robustly increases the nativeness of the code-switched phrase while suppressing accent leakage, and preserving overall speaker identity and na...

C. Lee, Sangkwon Park, Donghun Kang et al. · 1 citation

#artificial intelligence Preprint Aug 2026

HEAR Who Said What: Unlocking Speaker-Attributed Reasoning via Counterfactual Voice Grounding

A2R, a 30B model optimized on Counterfactual Audio with Speaker-level Hard negatives (CASH), a dataset designed to guide the model to prioritize acoustic vocal cues over linguistic signals, achieves strong performance on HEAR and exhibits zero-shot generalization to diverse multi-speaker downstream tasks, demonstrating...

Dongwook Lee, Sangkwon Park, Eunwoo Song et al. · 0 citations