Julien Epps

TL;DR: An organized study of information theoretic measures for clustering comparison, including several existing popular measures in the literature, as well as some newly proposed ones, and advocates the normalized information distance (NID) as a general measure of choice.

TL;DR: A basic standard acoustic parameter set for various areas of automatic voice analysis, such as paralinguistic or clinical speech analysis, is proposed and intended to provide a common baseline for evaluation of future research and eliminate differences caused by varying parameter sets or even different implementations of the same parameters.

TL;DR: This paper derives the analytical formula for the expected mutual information value between a pair of clusterings, and proposes the adjusted version for several popular information theoretic based measures.

TL;DR: How common paralinguistic speech characteristics are affected by depression and suicidality and the application of this information in classification and prediction systems is reviewed.

TL;DR: A new technique for the recognition of acceptor splice sites is proposed, which combines signal processing-based gene and exon prediction methods with an existing data-driven statistical method, and reveals a consistent reduction in false positives at different levels of sensitivity.