Emotional Style Transfer With Intensity Control in Zero-Shot TTS

Recent advancements in zero-shot text-to-speech have enabled the generated speech to preserve both the speaker's identity and emotion, guided by a reference speech. However, generating speech in emotional styles that the target speaker has never exhibited remains a central challenge in style transfer. Existing methods attempt to achieve this through disentangled modeling of speaker and style representations, yet insufficient disentanglement often leads to weak expressiveness and speaker leakage. To address these limitations, we propose a robust LM-based TTS framework that enables fine-grained control over speaker identity and emotional style. Specifically, we introduce a frame-level Style Transfer Pitch Predictor to capture fine-grained prosodic and speaker-related information, and a Speaker Emotional Style Alignment module that strengthens emotional expressiveness while preserving speaker identity. Our method enables precise control over the intensity of emotional style in the synthesized speech. Experimental results demonstrate that our approach outperforms existing methods, delivering more expressive and controllable emotional synthesized speech in the emotional style transfer task. Audio samples can be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://whyrrrrun.github.io/ICST.github.io/</uri>.