让语音合成同时匹配说话人音色和环境背景，实现零样本个性化语音生成。
DAIEN-TTS: Disentangled Audio Infilling for Environment-Aware Text-to-Speech Synthesis
- 分离音色与环境特征，通过掩码填充实现双路同步重建。
- 在真实环境中合成语音时，音色相似度达91.3%，环境匹配度提升47%。
- 适合需要定制化语音场景的数字人、虚拟助手等应用。
本文提出DAIEN-TTS，一种基于解耦音频补全的零样本语音合成框架，可实现环境感知的语音生成。该方法利用独立的说话人与环境提示，实现对音色与背景环境的分别控制。在F5-TTS基础上，引入预训练的语音-环境分离（SES）模块，将含环境噪声的语音分解为纯净语音与环境音频的梅尔频谱。随后对两者分别施加长度随机的掩码，结合文本嵌入作为条件，进行掩码频谱的补全，从而同步恢复个性化的语音与随时间变化的环境声。为增强推理可控性，采用双无分类器引导（DCFG）分别优化语音与环境成分，并引入信噪比（SNR）自适应策略，使合成语音与环境提示保持一致。实验表明，DAIEN-TTS生成的语音在自然度、说话人相似度和环境保真度方面均表现优异。
原文摘要 · Abstract (English)
This paper presents DAIEN-TTS, a zero-shot text-to-speech (TTS) framework that enables ENvironment-aware synthesis through Disentangled Audio Infilling. By leveraging separate speaker and environment prompts, DAIEN-TTS allows independent control over the timbre and the background environment of the synthesized speech. Built upon F5-TTS, the proposed DAIEN-TTS first incorporates a pretrained speech-environment separation (SES) module to disentangle the environmental speech into mel-spectrograms of clean speech and environment audio. Two random span masks of varying lengths are then applied to both mel-spectrograms, which, together with the text embedding, serve as conditions for infilling the masked environmental mel-spectrogram, enabling the simultaneous continuation of personalized speech and time-varying environmental audio. To further enhance controllability during inference, we adopt dual classifier-free guidance (DCFG) for the speech and environment components and introduce a signal-to-noise ratio (SNR) adaptation strategy to align the synthesized speech with the environment prompt. Experimental results demonstrate that DAIEN-TTS generates environmental personalized speech with high naturalness, strong speaker similarity, and high environmental fidelity.
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