<p>Results of the ablation studies.</p>

Existing systems often process textual and emotional conditions through shared or closely coupled pathways, which may introduce interference between semantic content and emotional expression. In addition, uniformly averaging per-sample Conditional Flow Matching (CFM) losses may provide insufficient optimization emphasis to high-loss emotional samples.

This study proposes a CFM-based zero-shot emotional speech synthesis method. Emotion–Text Decoupling Attention (ETDA) processes semantic and emotional conditions through parallel cross-attention streams and combines them through adaptive gated fusion, allowing the two conditions to retain their respective information before fusion. A 16-class fine-grained emotion space is constructed through classifier filtering and K-Means clustering based on pitch and energy features, and the resulting labels are mapped to continuous representations using a trainable lookup embedding.