Speech-Guided Multimodal Learning for Vocal Tract Segmentation in Real-Time MRI
For researchers and clinicians needing accurate vocal tract segmentation from rtMRI, this work provides a deployable solution that leverages multimodal training without requiring audio at inference.
The paper tackles vocal tract segmentation in real-time MRI, a challenging low-contrast dynamic segmentation problem. By using acoustic and phonological supervision during training but only requiring MRI at inference, the proposed method outperforms existing unimodal and multimodal approaches on two datasets.
Segmenting vocal tract articulators in real-time MRI (rtMRI) is a challenging dynamic image segmentation problem characterized by low contrast, rapid motion, and limited spatial resolution. However, while rtMRI acquisitions may provide synchronized acoustic signals, existing methods discard this information, and the few multimodal approaches that incorporate audio cannot be deployed when audio is unavailable. We propose a three-stage framework that leverages acoustic and phonological supervision during training while requiring only the rtMRI image at inference: phonological representations are converted into spatial bounding-box priors for articulator localization, visual and acoustic encoders are aligned via dual-level cross-modal contrastive pretraining, and the learned representations are fused through a cross-attention decoder, effectively transferring multimodal knowledge into a single-modality inference pipeline. Evaluated on 75-Speaker~Annot-16 and USC-TIMIT datasets, our method outperforms existing unimodal and multimodal methods, demonstrating that multimodal supervision provides transferable benefits for precise and clinically deployable vocal tract segmentation.