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arXiv · 2406.18625

Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer

Liming Wang·Yuan Gong·Nauman Dawalatabad·Marco Vilela·Katerina Placek·Brian Tracey·Yishu Gong·Alan Premasiri·Fernando Vieira·James Glass
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Abstract

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.

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Liming Wang, Yuan Gong, Nauman Dawalatabad, Marco Vilela, Katerina Placek, Brian Tracey, Yishu Gong, Alan Premasiri, Fernando Vieira, James Glass. 2024-06-26. Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer. https://arxiv.org/abs/2406.18625

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