title: Selecting Disorder-Specific Features for Speech Pathology Fingerprinting.
abstract: The general aim of this work is to learn a unique statistical signature for the state of a particular speech pathology. We pose this as a speaker identification problem for dysarthric individuals. To that end, we propose a novel algorithm for feature selection that aims to minimize the effects of speaker-specific features (e.g., fundamental frequency) and maximize the effects of pathology-specific features (e.g., vocal tract distortions and speech rhythm). We derive a cost function for optimizing feature selection that simultaneously trades off between these two competing criteria. Furthermore, we develop an efficient algorithm that optimizes this cost function and test the algorithm on a set of 34 dysarthric and 13 healthy speakers. Results show that the proposed method yields a set of features related to the speech disorder and not an individual's speaking style. When compared to other feature-selection algorithms, the proposed approach results in an improvement in a disorder fingerprinting task by selecting features that are specific to the disorder.
authors.affiliation: Department of Speech and Hearing Science, Arizona State University, Tempe, AZ 85287.
authors.forename: Visar
authors.initials: V
authors.lastname: Berisha
authors.affiliation: School of ECEE, SenSIP Center, Arizona State University, Tempe, AZ 85287.
authors.forename: Steven
authors.initials: S
authors.lastname: Sandoval
authors.affiliation: Department of Speech and Hearing Science, Arizona State University, Tempe, AZ 85287.
authors.forename: Rene
authors.initials: R
authors.lastname: Utianski
authors.affiliation: Department of Speech and Hearing Science, Arizona State University, Tempe, AZ 85287.
authors.forename: Julie
authors.initials: J
authors.lastname: Liss
authors.affiliation: School of ECEE, SenSIP Center, Arizona State University, Tempe, AZ 85287.
authors.forename: Andreas
authors.initials: A
authors.lastname: Spanias
keywords: dysarthria; feature selection; machine learning; speech pathology