MVP: Multi-source Voice Pathology detection
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913859642589184 |
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| author | Koudounas, Alkis La Quatra, Moreno Ciravegna, Gabriele Fantini, Marco Crosetti, Erika Succo, Giovanni Cerquitelli, Tania Siniscalchi, Sabato Marco Baralis, Elena |
| author_facet | Koudounas, Alkis La Quatra, Moreno Ciravegna, Gabriele Fantini, Marco Crosetti, Erika Succo, Giovanni Cerquitelli, Tania Siniscalchi, Sabato Marco Baralis, Elena |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20050 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MVP: Multi-source Voice Pathology detection Koudounas, Alkis La Quatra, Moreno Ciravegna, Gabriele Fantini, Marco Crosetti, Erika Succo, Giovanni Cerquitelli, Tania Siniscalchi, Sabato Marco Baralis, Elena Audio and Speech Processing Computation and Language 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. |
| title | MVP: Multi-source Voice Pathology detection |
| topic | Audio and Speech Processing Computation and Language |
| url | https://arxiv.org/abs/2505.20050 |