Emotional text-to-speech seeks to convey affect while preserving intelligibility and prosody, yet existing methods rely on coarse labels or proxy classifiers and receive only utterance-level feedback. We introduce Emotion-Aware Stepwise Preference Optimization (EASPO), a post-training framework that aligns diffusion TTS with fine-grained emotional preferences at intermediate denoising steps. Central to our approach is EASPM, a time-conditioned model that scores noisy intermediate speech states and enables automatic preference pair construction. EASPO optimizes generation to match these stepwise preferences, enabling controllable emotional shaping. Experiments show superior performance over existing methods in both expressiveness and naturalness.

翻译：情感文本转语音技术旨在传达情感的同时保持语音清晰度与韵律特征，但现有方法依赖粗粒度标签或代理分类器，且仅能获得语句级反馈。本文提出情感感知分步偏好优化框架，该后训练框架通过中间去噪步骤的细粒度情感偏好实现扩散式文本转语音模型的对齐。本方法的核心是情感感知分步偏好模型，该时序条件模型能够对含噪中间语音状态进行评分，并实现自动偏好对构建。情感感知分步偏好优化通过匹配这些分步偏好来优化生成过程，从而实现可控的情感塑造。实验结果表明，本方法在表达力与自然度方面均优于现有方法。