@inproceedings{7cf4317c6eb149989a051071ad911ad9,
title = "Objective Assessment of Vocal Tremor",
abstract = "Detecting early signs of neurodegeneration is vital for planning treatments for neurological diseases. Speech plays an important role in this context because it has been shown to be a promising early indicator of neurological decline, and because it can be acquired remotely without the need for specialized hardware. Typically, symptoms are characterized by clinicians using subjective and discrete scales. The poor resolution and subjectivity of these scales can make the earliest speech changes hard to detect. In this paper, we propose an algorithm for the objective assessment of vocal tremor, a phenomenon associated with many neurological disorders. The algorithm extracts and aggregates a feature set from the average spectra of the energy and fundamental frequency profiles of a sustained phonation. We show that the resultant low-dimensional feature set reliably classifies healthy controls and patients with amyotrophic lateral sclerosis perceptually rated for tremor by speech language pathologists.",
keywords = "Amyotrophic Lateral Sclerosis (ALS), Dysarthria, Speech, Tremor",
author = "Jacob Peplinski and Visar Berisha and Julie Liss and Shira Hahn and Jeremy Shefner and Seward Rutkove and Kristin Qi and Kerisa Shelton",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICASSP.2019.8682995",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6386--6390",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
}