AI RESEARCH
Data-efficient Targeted Token-level Preference Optimization for LLM-based Text-to-Speech
arXiv CS.AI
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ArXi:2510.05799v2 Announce Type: replace-cross Aligning text-to-speech (TTS) system outputs with human feedback through preference optimization has been shown to effectively improve the robustness and naturalness of language model-based TTS models. Current approaches primarily require paired desirable and undesirable samples at the utterance level. However, such pairs are often limited in TTS output data, and utterance-level formulation prevents fine-grained token-level optimization needed for accurate pronunciation alignment.