Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866913406183800832 |
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| author | Wang, Liming Gong, Yuan Dawalatabad, Nauman Vilela, Marco Placek, Katerina Tracey, Brian Gong, Yishu Premasiri, Alan Vieira, Fernando Glass, James |
| author_facet | Wang, Liming Gong, Yuan Dawalatabad, Nauman Vilela, Marco Placek, Katerina Tracey, Brian Gong, Yishu Premasiri, Alan Vieira, Fernando Glass, James |
| contents | Automatic prediction of amyotrophic lateral sclerosis (ALS) disease progression provides a more efficient and objective alternative than manual approaches. We propose ALS longitudinal speech transformer (ALST), a neural network-based automatic predictor of ALS disease progression from longitudinal speech recordings of ALS patients. By taking advantage of high-quality pretrained speech features and longitudinal information in the recordings, our best model achieves 91.0\% AUC, improving upon the previous best model by 5.6\% relative on the ALS TDI dataset. Careful analysis reveals that ALST is capable of fine-grained and interpretable predictions of ALS progression, especially for distinguishing between rarer and more severe cases. Code is publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18625 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer Wang, Liming Gong, Yuan Dawalatabad, Nauman Vilela, Marco Placek, Katerina Tracey, Brian Gong, Yishu Premasiri, Alan Vieira, Fernando Glass, James Sound Artificial Intelligence Audio and Speech Processing Automatic prediction of amyotrophic lateral sclerosis (ALS) disease progression provides a more efficient and objective alternative than manual approaches. We propose ALS longitudinal speech transformer (ALST), a neural network-based automatic predictor of ALS disease progression from longitudinal speech recordings of ALS patients. By taking advantage of high-quality pretrained speech features and longitudinal information in the recordings, our best model achieves 91.0\% AUC, improving upon the previous best model by 5.6\% relative on the ALS TDI dataset. Careful analysis reveals that ALST is capable of fine-grained and interpretable predictions of ALS progression, especially for distinguishing between rarer and more severe cases. Code is publicly available. |
| title | Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2406.18625 |