Beyond Global Emotion: Fine-Grained Emotional Speech Synthesis with Dynamic Word-Level Modulation

• URL: http://arxiv.org/abs/2509.20378v1
• Date: Sat, 20 Sep 2025 14:26:15 GMT
• Title: Beyond Global Emotion: Fine-Grained Emotional Speech Synthesis with Dynamic Word-Level Modulation
• Authors: Sirui Wang, Andong Chen, Tiejun Zhao,
• Abstract summary: Emotional text-to-speech (E-TTS) is central to creating natural and trustworthy human-computer interaction.<n>We introduce Emo-FiLM, a fine-grained emotion modeling framework for LLM-based TTS.<n>Emo-FiLM aligns frame-level features from emotion2vec to words to obtain word-level emotion annotations.<n>It maps them through a Feature-wise Linear Modulation layer, enabling word-level emotion control by directly modulating text embeddings.
• Score: 27.668177917370144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotional text-to-speech (E-TTS) is central to creating natural and trustworthy human-computer interaction. Existing systems typically rely on sentence-level control through predefined labels, reference audio, or natural language prompts. While effective for global emotion expression, these approaches fail to capture dynamic shifts within a sentence. To address this limitation, we introduce Emo-FiLM, a fine-grained emotion modeling framework for LLM-based TTS. Emo-FiLM aligns frame-level features from emotion2vec to words to obtain word-level emotion annotations, and maps them through a Feature-wise Linear Modulation (FiLM) layer, enabling word-level emotion control by directly modulating text embeddings. To support evaluation, we construct the Fine-grained Emotion Dynamics Dataset (FEDD) with detailed annotations of emotional transitions. Experiments show that Emo-FiLM outperforms existing approaches on both global and fine-grained tasks, demonstrating its effectiveness and generality for expressive speech synthesis.

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