title: Towards A Clinical Tool For Automatic Intelligibility Assessment.
abstract: An important, yet under-explored, problem in speech processing is the automatic assessment of intelligibility for pathological speech. In practice, intelligibility assessment is often done through subjective tests administered by speech pathologists; however research has shown that these tests are inconsistent, costly, and exhibit poor reliability. Although some automatic methods for intelligibility assessment for telecommunications exist, research specific to pathological speech has been limited. Here, we propose an algorithm that captures important multi-scale perceptual cues shown to correlate well with intelligibility. Nonlinear classifiers are trained at each time scale and a final intelligibility decision is made using ensemble learning methods from machine learning. Preliminary results indicate a marked improvement in intelligibility assessment over published baseline results.
authors.affiliation: Department of Speech and Hearing Science, Arizona State University.
authors.forename: Visar
authors.initials: V
authors.lastname: Berisha
authors.affiliation: Department of Speech and Hearing Science, Arizona State University.
authors.forename: Rene
authors.initials: R
authors.lastname: Utianski
authors.affiliation: Department of Speech and Hearing Science, Arizona State University.
authors.forename: Julie
authors.initials: J
authors.lastname: Liss
keywords: intelligibility assessment; machine learning; multi-scale analysis; speech pathology