Towards a clinical tool for automatic intelligibility assessment

Visar Berisha, Rene Utianski, Julie Liss

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages2825-2828
Number of pages4
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • intelligibility assessment
  • machine learning
  • multi-scale analysis
  • speech pathology

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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