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The IFF-TE method with fine-tuned backbones achieves the highest AUC scores: 95.8% (SVD), 96.3% (A VFAD), and 93.6% (IPV)

Experimental Analysis Table 1 demonstrates that our multi-source approach significantly outperforms single-source baselines across all datasets

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MVP: Multi-source Voice Pathology detection

eess.AS · 2025-05-26 · conditional · novelty 5.0

Combining sustained vowel and sentence reading recordings through intermediate feature fusion with transformers improves voice pathology detection AUC by up to 13% over single-source methods.

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  • MVP: Multi-source Voice Pathology detection eess.AS · 2025-05-26 · conditional · none · ref 4

    Combining sustained vowel and sentence reading recordings through intermediate feature fusion with transformers improves voice pathology detection AUC by up to 13% over single-source methods.