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arXiv · 2610.30477

EmoFI-TTS: Face-Conditioned Expressive Voice Synthesis with Intensity Modulation

Yejin Jeon·Luel Hagos Beyene·Marie Maltais·Hyounghun Kim·David Ifeoluwa Adelani
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Abstract

Controlling emotional intensity remains challenging in expressive text-to-speech synthesis, particularly when emotion must be inferred without explicit labels. We propose EmoFI-TTS, a face-conditioned framework that derives emotion category and intensity from a single facial image while extracting speaker characteristics from reference audio. Modality-specific representations are fused to condition a pretrained speech synthesizer, with cross-speaker training encouraging separation of affect and vocal identity. An intensity-conditioned refinement module enhances the generated mel-spectrogram, while interpolation of learned intensity embeddings enables continuous control without additional training. On speaker-disjoint MEAD evaluation, EmoFI-TTS achieves on average 64.4% and 75.2% objective and subjective emotion intensity accuracies, while retaining low word error rate and high naturalness.

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Yejin Jeon, Luel Hagos Beyene, Marie Maltais, Hyounghun Kim, David Ifeoluwa Adelani. 2026-10-21. EmoFI-TTS: Face-Conditioned Expressive Voice Synthesis with Intensity Modulation. https://arxiv.org/abs/2610.30477

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