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English(EN) Diagnose, Then Refine: A Closed-Loop TTS System with AudioLLM-Guided Correction

新的LoopTTS系统使用AudioLLM来优化合成语音质量

研究人员开发了LoopTTS,一个新颖的闭环文本到语音(TTS)系统,旨在纠正合成音频中的韵律缺陷。该系统利用AudioLLM作为裁判来识别重音错位或不自然停顿等问题,然后采用细粒度指令遵循TTS模型Refiner,通过引导式纠正来重新合成音频。一个新的包含42,000个标注示例的数据集Refiner-DB被创建用于训练Refiner,该系统展示了比现有方法更高的音频质量和更好的韵律控制。 AI

影响 这种闭环TTS系统可能带来更自然、更富表现力的合成语音,从而改善各种应用中的可访问性和用户体验。

排序理由 该集群包含一篇详细介绍TTS新系统和数据集的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的LoopTTS系统使用AudioLLM来优化合成语音质量

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该集群包含一篇详细介绍TTS新系统和数据集的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zeyang Song, Tianchi Liu, Tianrui Wang, Chenglin Xu, Steven Y. Guo, Haizhou Li ·

    诊断后优化:基于AudioLLM引导的闭环TTS系统

    arXiv:2608.28970v1 Announce Type: cross Abstract: Current TTS systems typically rely on open-loop, single-pass generation and can produce sporadic local prosodic defects, such as misplaced stress, unnatural pauses, or flattened intonation, that utterance-level metrics often fail …