用于TTS模型事后韵律与误音纠正的反事实激活编辑
Counterfactual Activation Editing for Post-hoc Prosody and Mispronunciation Correction in TTS Models
AI总结：
提出反事实激活编辑方法，通过操作预训练TTS模型的内部表示实现事后韵律控制与误音纠正，无需重训即可在推理时优化输出。
摘要：
文本转语音（TTS）的最新进展显著提升了语音自然度，增加了对精确韵律控制和误音纠正的需求。现有的韵律操作方法通常依赖专用模块或额外训练，限制了其事后调整的能力。类似地，传统的误音纠正依赖字素到音素字典，使其在低资源环境中不够实用。我们引入了反事实激活编辑，这是一种与模型无关的方法，通过操作预训练TTS模型中的内部表示，实现对韵律和发音的事后控制。实验结果表明，我们的方法在保持合成质量的同时，能有效调整韵律特征并纠正误音。这为无需重新训练的TTS输出推理时优化打开了大门，弥合了预训练TTS模型与可编辑语音合成之间的差距。
英文摘要：
Recent advances in Text-to-Speech (TTS) have significantly improved speech naturalness, increasing the demand for precise prosody control and mispronunciation correction. Existing approaches for prosody manipulation often depend on specialized modules or additional training, limiting their capacity for post-hoc adjustments. Similarly, traditional mispronunciation correction relies on grapheme-to-phoneme dictionaries, making it less practical in low-resource settings. We introduce Counterfactual Activation Editing, a model-agnostic method that manipulates internal representations in a pre-trained TTS model to achieve post-hoc control of prosody and pronunciation. Experimental results show that our method effectively adjusts prosodic features and corrects mispronunciations while preserving synthesis quality. This opens the door to inference-time refinement of TTS outputs without retraining, bridging the gap between pre-trained TTS models and editable speech synthesis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
