Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.16445 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917482785144832 |
|---|---|
| author | Sannino, Giovanna De Falco, Ivanoe Brancati, Nadia Verde, Laura Frucci, Maria Riccio, Daniel Bevilacqua, Vincenzo Di Marino, Antonio Aruta, Lucia Iuzzolino, Valentina Virginia Senerchia, Gianmaria Spisto, Myriam Dubbioso, Raffaele |
| author_facet | Sannino, Giovanna De Falco, Ivanoe Brancati, Nadia Verde, Laura Frucci, Maria Riccio, Daniel Bevilacqua, Vincenzo Di Marino, Antonio Aruta, Lucia Iuzzolino, Valentina Virginia Senerchia, Gianmaria Spisto, Myriam Dubbioso, Raffaele |
| contents | Recent advances in Artificial Intelligence (AI) and the exploration of noninvasive, objective biomarkers, such as speech signals, have encouraged the development of algorithms to support the early diagnosis of neurodegenerative diseases, including Amyotrophic Lateral Sclerosis (ALS). Voice changes in subjects suffering from ALS typically manifest as progressive dysarthria, which is a prominent neurodegenerative symptom because it affects patients as the disease progresses. Since voice signals are complex data, the development and use of advanced AI techniques are fundamental to extracting distinctive patterns from them. Validating AI algorithms for ALS diagnosis and monitoring using voice signals is challenging, particularly due to the lack of annotated reference datasets. In this work, we present the outcome of a collaboration between a multidisciplinary team of clinicians and Machine Learning experts to create both a clinically annotated validation dataset and the "Speech Analysis for Neurodegenerative Diseases" (SAND) challenge based on it. Specifically, by analyzing voice disorders, the SAND challenge provides an opportunity to develop, test, and evaluate AI models for the automatic early identification and prediction of ALS disease progression. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16445 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment Sannino, Giovanna De Falco, Ivanoe Brancati, Nadia Verde, Laura Frucci, Maria Riccio, Daniel Bevilacqua, Vincenzo Di Marino, Antonio Aruta, Lucia Iuzzolino, Valentina Virginia Senerchia, Gianmaria Spisto, Myriam Dubbioso, Raffaele Audio and Speech Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Recent advances in Artificial Intelligence (AI) and the exploration of noninvasive, objective biomarkers, such as speech signals, have encouraged the development of algorithms to support the early diagnosis of neurodegenerative diseases, including Amyotrophic Lateral Sclerosis (ALS). Voice changes in subjects suffering from ALS typically manifest as progressive dysarthria, which is a prominent neurodegenerative symptom because it affects patients as the disease progresses. Since voice signals are complex data, the development and use of advanced AI techniques are fundamental to extracting distinctive patterns from them. Validating AI algorithms for ALS diagnosis and monitoring using voice signals is challenging, particularly due to the lack of annotated reference datasets. In this work, we present the outcome of a collaboration between a multidisciplinary team of clinicians and Machine Learning experts to create both a clinically annotated validation dataset and the "Speech Analysis for Neurodegenerative Diseases" (SAND) challenge based on it. Specifically, by analyzing voice disorders, the SAND challenge provides an opportunity to develop, test, and evaluate AI models for the automatic early identification and prediction of ALS disease progression. |
| title | SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment |
| topic | Audio and Speech Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2604.16445 |