title: Modeling Pathological Speech Perception From Data With Similarity Labels.
abstract: The current state of the art in judging pathological speech intelligibility is subjective assessment performed by trained speech pathologists (SLP). These tests, however, are inconsistent, costly and, oftentimes suffer from poor intra- and inter-judge reliability. As such, consistent, reliable, and perceptually-relevant objective evaluations of pathological speech are critical. Here, we propose a data-driven approach to this problem. We propose new cost functions for examining data from a series of experiments, whereby we ask certified SLPs to rate pathological speech along the perceptual dimensions that contribute to decreased intelligibility. We consider qualitative feedback from SLPs in the form of comparisons similar to statements "Is Speaker A's rhythm more similar to Speaker B or Speaker C?" Data of this form is common in behavioral research, but is different from the traditional data structures expected in supervised (data matrix + class labels) or unsupervised (data matrix) machine learning. The proposed method identifies relevant acoustic features that correlate with the ordinal data collected during the experiment. Using these features, we show that we are able to develop objective measures of the speech signal degradation that correlate well with SLP responses.
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: 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: 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: School of ECEE, SenSIP Center, Arizona State University, Tempe, AZ 85287.
authors.forename: Andreas
authors.initials: A
authors.lastname: Spanias