Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer

Autor:	Wang, Liming, Gong, Yuan, Dawalatabad, Nauman, Vilela, Marco, Placek, Katerina, Tracey, Brian, Gong, Yishu, Premasiri, Alan, Vieira, Fernando, Glass, James
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.18625 Zobrazit plný text záznamu View this record from Arxiv