MVP: Multi-source Voice Pathology detection

• URL: http://arxiv.org/abs/2505.20050v1
• Date: Mon, 26 May 2025 14:38:35 GMT
• Title: MVP: Multi-source Voice Pathology detection
• Authors: Alkis Koudounas, Moreno La Quatra, Gabriele Ciravegna, Marco Fantini, Erika Crosetti, Giovanni Succo, Tania Cerquitelli, Sabato Marco Siniscalchi, Elena Baralis,
• Abstract summary: MVP (Multi-source Voice Pathology detection) is a novel approach that leverages transformers operating directly on raw voice signals.<n>Our approach achieves up to +13% AUC improvement over single-source methods.
• Score: 18.49926939309
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Voice disorders significantly impact patient quality of life, yet non-invasive automated diagnosis remains under-explored due to both the scarcity of pathological voice data, and the variability in recording sources. This work introduces MVP (Multi-source Voice Pathology detection), a novel approach that leverages transformers operating directly on raw voice signals. We explore three fusion strategies to combine sentence reading and sustained vowel recordings: waveform concatenation, intermediate feature fusion, and decision-level combination. Empirical validation across the German, Portuguese, and Italian languages shows that intermediate feature fusion using transformers best captures the complementary characteristics of both recording types. Our approach achieves up to +13% AUC improvement over single-source methods.

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