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arXiv · 2509.13767

VocSegMRI: Multimodal Learning for Precise Vocal Tract Segmentation in Real-time MRI

Daiqi Liu·Johannes Enk·Maureen Stone·Fangxu Xing·Tomás Arias-Vergara·Jerry L. Prince·Jana Hutter·Jonghye Woo·Andreas Maier·Paula Andrea Pérez-Toro
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Abstract

Accurate segmentation of articulatory structures in real-time MRI (rtMRI) remains challenging, as existing methods rely primarily on visual cues and overlook complementary information from synchronized speech signals. We propose VocSegMRI, a multimodal framework integrating video, audio, and phonological inputs via cross-attention fusion and a contrastive learning objective that improves cross-modal alignment and segmentation precision. Evaluated on USC-75 and further validated via zero-shot transfer on USC-TIMIT, VocSegMRI outperforms unimodal and multimodal baselines, with ablations confirming the contribution of each component.

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Daiqi Liu, Johannes Enk, Maureen Stone, Fangxu Xing, Tomás Arias-Vergara, Jerry L. Prince, Jana Hutter, Jonghye Woo, Andreas Maier, Paula Andrea Pérez-Toro. 2025-09-17. VocSegMRI: Multimodal Learning for Precise Vocal Tract Segmentation in Real-time MRI. https://arxiv.org/abs/2509.13767

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