Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer
<|reference_start|>Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer: 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.<|reference_end|>
@article{wang2024automatic,
    title={Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using
  Longitudinal Speech Transformer},
    author={Liming Wang, Yuan Gong, Nauman Dawalatabad, Marco Vilela, Katerina
  Placek, Brian Tracey, Yishu Gong, Alan Premasiri, Fernando Vieira, James
  Glass},
    journal={arXiv preprint arXiv:2406.18625},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18625},
    primaryClass={cs.SD cs.AI eess.AS}
}