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We inte","title":"AuEmoChat: Authentic Emotion Understanding and Rendering for Conversational Speech Synthesis","url":"https://arxiv.org/abs/2607.15755","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.15755v1 Announce Type: cross \nAbstract: Conversational Speech Synthesis (CSS) aims to synthesize speech with human-like emotional expression and contextual consistency in user-agent interactions. Existing CSS methods struggle to render authentic human emotions due to limited predefined emotion label spaces (e.g., seven emotion categories), while redundant multimodal tokens in multi-turn dialogue history interfere with context understanding. To address these issues, we propose AuEmoChat, a CSS framework for authentic emotion understanding and rendering. First, we develop AuEmoCodec, which learns a discrete authentic emotion token space from large-scale emotional speech via finite scalar quantization, enabling a more authentic emotion representation than limited basic emotion categories. 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