Preference Optimization for Non-Verbal Vocalization Synthesis

The paper investigates how to optimize non‑verbal vocalizations (NVs) such as laughter, coughs, and sighs in text‑to‑speech systems. It introduces a preference‑optimization framework that uses preference signals, pair construction, and DPO objectives, and defines an NV‑aware character error rate (NV‑CER) to guide controllable NV generation. Experiments on Emilia‑NV and NV‑Bench demonstrate that the proposed setup improves NV realization and lexical fidelity, with objective, LLM‑based, and human evaluations supporting the results.

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• llms
• reinforcement-learning