VocSegMRI: Multimodal Learning for Precise Vocal Tract Segmentation in Real-time MRI
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 17 Sep 2025.
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References · 14
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Citing papers per year: 2026: 2.
2 papers.