12 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages915-919
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

Fingerprint

Labels
Behavioral research
Speech intelligibility
Cost functions
Data structures
Learning systems
Acoustics
Experiments
Feedback
Degradation

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Cite this

Berisha, V., Liss, J., Sandoval, S., Utianski, R., & Spanias, A. (2014). Modeling pathological speech perception from data with similarity labels. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 915-919). [6853730] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2014.6853730

Modeling pathological speech perception from data with similarity labels. / Berisha, Visar; Liss, Julie; Sandoval, Steven; Utianski, Rene; Spanias, Andreas.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 915-919 6853730.

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

Berisha, V, Liss, J, Sandoval, S, Utianski, R & Spanias, A 2014, Modeling pathological speech perception from data with similarity labels. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings., 6853730, Institute of Electrical and Electronics Engineers Inc., pp. 915-919, 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014, Florence, Italy, 5/4/14. https://doi.org/10.1109/ICASSP.2014.6853730
Berisha V, Liss J, Sandoval S, Utianski R, Spanias A. Modeling pathological speech perception from data with similarity labels. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. p. 915-919. 6853730 https://doi.org/10.1109/ICASSP.2014.6853730
Berisha, Visar ; Liss, Julie ; Sandoval, Steven ; Utianski, Rene ; Spanias, Andreas. / Modeling pathological speech perception from data with similarity labels. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 915-919
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