A Concept-based approach to Voice Disorder 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_ | 1866908463634841600 |
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| author | Ghia, Davide Ciravegna, Gabriele Koudounas, Alkis Fantini, Marco Crosetti, Erika Succo, Giovanni Cerquitelli, Tania |
| author_facet | Ghia, Davide Ciravegna, Gabriele Koudounas, Alkis Fantini, Marco Crosetti, Erika Succo, Giovanni Cerquitelli, Tania |
| contents | Voice disorders affect a significant portion of the population, and the ability to diagnose them using automated, non-invasive techniques would represent a substantial advancement in healthcare, improving the quality of life of patients. Recent studies have demonstrated that artificial intelligence models, particularly Deep Neural Networks (DNNs), can effectively address this task. However, due to their complexity, the decision-making process of such models often remain opaque, limiting their trustworthiness in clinical contexts. This paper investigates an alternative approach based on Explainable AI (XAI), a field that aims to improve the interpretability of DNNs by providing different forms of explanations. Specifically, this works focuses on concept-based models such as Concept Bottleneck Model (CBM) and Concept Embedding Model (CEM) and how they can achieve performance comparable to traditional deep learning methods, while offering a more transparent and interpretable decision framework. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_17799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Concept-based approach to Voice Disorder Detection Ghia, Davide Ciravegna, Gabriele Koudounas, Alkis Fantini, Marco Crosetti, Erika Succo, Giovanni Cerquitelli, Tania Audio and Speech Processing Machine Learning Sound Voice disorders affect a significant portion of the population, and the ability to diagnose them using automated, non-invasive techniques would represent a substantial advancement in healthcare, improving the quality of life of patients. Recent studies have demonstrated that artificial intelligence models, particularly Deep Neural Networks (DNNs), can effectively address this task. However, due to their complexity, the decision-making process of such models often remain opaque, limiting their trustworthiness in clinical contexts. This paper investigates an alternative approach based on Explainable AI (XAI), a field that aims to improve the interpretability of DNNs by providing different forms of explanations. Specifically, this works focuses on concept-based models such as Concept Bottleneck Model (CBM) and Concept Embedding Model (CEM) and how they can achieve performance comparable to traditional deep learning methods, while offering a more transparent and interpretable decision framework. |
| title | A Concept-based approach to Voice Disorder Detection |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2507.17799 |