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    <abstract>Translating song lyrics while preserving singability requires satisfying musical constraints, including syllable counts, rhyme schemes, and syllable patterns, that standard machine translation ignores. We present a framework that exposes IPA-based phonetic analysis as a suite of composable Agent Skills, each defined by a natural-language description plus deterministic verification scripts that the orchestrating LLM invokes on demand. The agent extracts constraints from source lyrics, then translates through a multi-phase process, calling skills to verify syllable counts and rhyme and iteratively refine syllable-count mismatches. We formalize the three musical constraints, describe the IPA skill suite and agent architecture, and propose three evaluation metrics: Syllable Error Rate (SER), Syllable Count Relative Error (SCRE), and Adjusted Rand Index (ARI). Experiments across three frontier orchestrators, with component ablations, confirm that tool-verified translation dramatically improves constraint satisfaction and preserves rhyme structure substantially better than a supervised baseline, while requiring no task-specific training and no parallel corpora, and transferring to Spanish and Japanese targets with only a small language-specific syllabification adapter.</abstract>
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%0 Conference Proceedings
%T IPA-Verified Singable Lyrics Translation with LLM Agents
%A Chiu, Guan-Ming
%A Chiao-Chih, Cheng
%A Lee, Kuan-Wei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chiu-etal-2026-ipa
%X Translating song lyrics while preserving singability requires satisfying musical constraints, including syllable counts, rhyme schemes, and syllable patterns, that standard machine translation ignores. We present a framework that exposes IPA-based phonetic analysis as a suite of composable Agent Skills, each defined by a natural-language description plus deterministic verification scripts that the orchestrating LLM invokes on demand. The agent extracts constraints from source lyrics, then translates through a multi-phase process, calling skills to verify syllable counts and rhyme and iteratively refine syllable-count mismatches. We formalize the three musical constraints, describe the IPA skill suite and agent architecture, and propose three evaluation metrics: Syllable Error Rate (SER), Syllable Count Relative Error (SCRE), and Adjusted Rand Index (ARI). Experiments across three frontier orchestrators, with component ablations, confirm that tool-verified translation dramatically improves constraint satisfaction and preserves rhyme structure substantially better than a supervised baseline, while requiring no task-specific training and no parallel corpora, and transferring to Spanish and Japanese targets with only a small language-specific syllabification adapter.
%R 10.18653/2026.findings-emnlp.1174
%U https://aclanthology.org/2026.findings-emnlp.1174/
%U https://doi.org/10.18653/2026.findings-emnlp.1174
%P 24826-24841

Markdown (Informal)

[IPA-Verified Singable Lyrics Translation with LLM Agents](https://aclanthology.org/2026.findings-emnlp.1174/) (Chiu et al., Findings 2026)

• IPA-Verified Singable Lyrics Translation with LLM Agents (Chiu et al., Findings 2026)

ACL

• Guan-Ming Chiu, Cheng Chiao-Chih, and Kuan-Wei Lee. 2026. IPA-Verified Singable Lyrics Translation with LLM Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 24826–24841, Budapest, Hungary. Association for Computational Linguistics.