Psychological research on emotion dynamics has established that human affect is a continuous, evolving process: emotions rise, decay, and transition within seconds. Current emotional text-to-speech (TTS) systems, however, condition on a single discrete label or static embedding per utterance, fundamentally misaligning with the temporal nature of affect. While recent LLM-based TTS systems may implicitly vary prosody through text understanding, such variation is neither explicitly controllable nor precise enough for targeted intra-utterance transitions. We address three challenges: (1) a multi-pass flow blending pipeline synthesizes frame-aligned transition audio, circumventing the scarcity of natural intra-utterance transitions; (2) dual-stage Valence–Arousal–Dominance (VAD) conditioning guides prosodic planning in the LLM and acoustic realization in the flow decoder via frame-level VAD embeddings; (3) direction–magnitude decoupled injection structurally separates emotion direction from injection magnitude, preventing content degradation. EmoTra-TTS adds only +0.43% parameters with no latency overhead, achieves 30%–87% relative improvement on emotion transition quality, corroborated by 67.5%–79.5% overall win rates in a pairwise preference test against four SOTA baselines. The complete code, including data synthesis and training pipelines, will be released after the review process.

In the field of natural language generation (NLG), controlling text generation (CTG) is critical, particularly in query auto-completion (QAC) where the need for personalization and diversity is paramount. However, it is essentially challenging to adapt to various control objectives and constraints, which results in existing CTG approaches meeting with mixed success. This paper presents UCTG, a unified controllable text generation framework, which introduces a novel prompt learning method for CTG. Specifically, this framework seamlessly integrates a control module, a prompt module, and a generation module. The control module leverages a fine-tuned model to distill user preference features and behavioral patterns from historical data, incorporating human feedback into the model’s loss functions. These features are then transformed by the prompt module into vectors that guide the generation module. As such, the text generation can be flexibly controlled without modifying the task settings. By employing this unified approach, UCTG significantly improves query accuracy and coherence in tasks with different objectives and constraints, which is validated by extensive experiments on the Meituan and AOL real-world datasets. UCTG not only improves text generation control in QAC but also sets a new framework for flexible NLG applications.