Voice Disorder Analysis: a Transformer-based Approach
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912028050849792 |
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| author | Koudounas, Alkis Ciravegna, Gabriele Fantini, Marco Succo, Giovanni Crosetti, Erika Cerquitelli, Tania Baralis, Elena |
| author_facet | Koudounas, Alkis Ciravegna, Gabriele Fantini, Marco Succo, Giovanni Crosetti, Erika Cerquitelli, Tania Baralis, Elena |
| contents | Voice disorders are pathologies significantly affecting patient quality of life. However, non-invasive automated diagnosis of these pathologies is still under-explored, due to both a shortage of pathological voice data, and diversity of the recording types used for the diagnosis. This paper proposes a novel solution that adopts transformers directly working on raw voice signals and addresses data shortage through synthetic data generation and data augmentation. Further, we consider many recording types at the same time, such as sentence reading and sustained vowel emission, by employing a Mixture of Expert ensemble to align the predictions on different data types. The experimental results, obtained on both public and private datasets, show the effectiveness of our solution in the disorder detection and classification tasks and largely improve over existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14693 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Voice Disorder Analysis: a Transformer-based Approach Koudounas, Alkis Ciravegna, Gabriele Fantini, Marco Succo, Giovanni Crosetti, Erika Cerquitelli, Tania Baralis, Elena Audio and Speech Processing Machine Learning Voice disorders are pathologies significantly affecting patient quality of life. However, non-invasive automated diagnosis of these pathologies is still under-explored, due to both a shortage of pathological voice data, and diversity of the recording types used for the diagnosis. This paper proposes a novel solution that adopts transformers directly working on raw voice signals and addresses data shortage through synthetic data generation and data augmentation. Further, we consider many recording types at the same time, such as sentence reading and sustained vowel emission, by employing a Mixture of Expert ensemble to align the predictions on different data types. The experimental results, obtained on both public and private datasets, show the effectiveness of our solution in the disorder detection and classification tasks and largely improve over existing approaches. |
| title | Voice Disorder Analysis: a Transformer-based Approach |
| topic | Audio and Speech Processing Machine Learning |
| url | https://arxiv.org/abs/2406.14693 |