Diffusion and flow-matching based text-to-speech (TTS) models excel in naturalness but often lack explicit emotion control, as emotional signals remain entangled with speaker identity. We discover that emotion embedding emerges as a linearly decodable direction of frozen hidden states, nearly orthogonal to the direction embedding speaker identity. This inspires a plug-and-play framework DUET for emotion control over pretrained diffusion and flow-matching based TTS models. During generation, DUET unifies dual-space control to achieve fine-grained emotion intervention in a single per-step update: hidden space steering shifts generation along the target emotion direction, while mel-space guidance refines spectral details through gradients backpropagated from a differentiable vocoder. We validate DUET on five architecturally diverse pretrained TTS backbones across three datasets, where it outperforms 10 supervised state-of-the-art emotional TTS baselines across paradigms and achieves the highest human-rated emotion appropriateness. To further showcase its qualitative behavior, we deploy DUET on an Ameca humanoid robot, where it produces richly expressive emotional speech on the humanoid, demonstrating the strong potential for plug-and-play affective interaction for embodied agents.

翻译：摘要：基于扩散和流匹配的文本转语音（TTS）模型在自然度方面表现优异，但往往缺乏显式的情感控制能力，因为情感信号与说话人身份仍相互纠缠。我们发现，情感嵌入可作为冻结隐状态中线性可解码的方向自然涌现，且该方向与编码说话人身份的方向近乎正交。这一发现启发了即插即用框架DUET，用于对预训练扩散与流匹配TTS模型进行情感控制。在生成过程中，DUET统一双空间控制机制，通过单步逐次更新实现细粒度情感干预：隐空间引导沿目标情感方向偏移生成过程，而梅尔谱空间引导则通过可微分声码器的反向传播梯度细化频谱细节。我们在三个数据集上对五种架构迥异的预训练TTS主干模型验证了DUET，其表现超越了横跨不同范式的10种有监督情感TTS基线方法，并获得了人类评分中最高等级的情感适切性。为进一步展示定性效果，我们将DUET部署至Ameca人形机器人，使其生成丰富情感表达的语言，充分展现了即插即用情感交互在具身智能体中的巨大潜力。