J
Jun Wang
Researcher at University of Texas at Austin
Publications - 86
Citations - 2386
Jun Wang is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Speech production & Amyotrophic lateral sclerosis. The author has an hindex of 25, co-authored 84 publications receiving 1795 citations. Previous affiliations of Jun Wang include University of Texas at Dallas & University of Nebraska–Lincoln.
Papers
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Journal ArticleDOI
Generalizing DTW to the multi-dimensional case requires an adaptive approach
TL;DR: The two most commonly used multi-dimensional DTW methods can produce different classifications, and neither one dominates over the other, and the method allowed to ensure that classification results are at least as accurate as the better of the two rival methods, and, in many cases, significantly more accurate.
Journal ArticleDOI
Bulbar and speech motor assessment in ALS: Challenges and future directions
Jordan R. Green,Yana Yunusova,Mili S. Kuruvilla,Jun Wang,Gary L. Pattee,Lori Synhorst,Lorne Zinman,James D. Berry +7 more
TL;DR: In this paper, the authors considered objective measures of speech motor function, which show promise for forming the basis of a comprehensive, quantitative bulbar motor assessment in ALS, based on the assessment of four speech subsystems: respiratory, phonatory, articulatory, and resonatory.
Proceedings Article
On the non-trivial generalization of Dynamic Time Warping to the multi-dimensional case
TL;DR: The two most commonly used multidimensional DTW methods can produce different classifications, and neither one dominates over the other; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods to give credence to.
Proceedings ArticleDOI
Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy
TL;DR: This work proposes a novel pruning strategy that exploits both upper and lower bounds to prune off a large fraction of the expensive distance calculations, and shows that it can be used to order the unavoidable calculations in a most-useful-first ordering, thus casting the clustering as an anytime algorithm.
Journal ArticleDOI
Predicting Early Bulbar Decline in Amyotrophic Lateral Sclerosis: A Speech Subsystem Approach.
TL;DR: The articulatory and phonatory predictors are sensitive indicators of early bulbar decline due to ALS, which has implications for predicting disease onset and progression and clinical management of ALS.