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Research Article
9 November 2017

Predicting Intelligibility Gains in Dysarthria Through Automated Speech Feature Analysis

Publication: Journal of Speech, Language, and Hearing Research
Volume 60, Number 11
Pages 3058-3068

Abstract

Purpose

Behavioral speech modifications have variable effects on the intelligibility of speakers with dysarthria. In the companion article, a significant relationship was found between measures of speakers' baseline speech and their intelligibility gains following cues to speak louder and reduce rate (Fletcher, McAuliffe, Lansford, Sinex, & Liss, 2017). This study reexamines these features and assesses whether automated acoustic assessments can also be used to predict intelligibility gains.

Method

Fifty speakers (7 older individuals and 43 with dysarthria) read a passage in habitual, loud, and slow speaking modes. Automated measurements of long-term average spectra, envelope modulation spectra, and Mel-frequency cepstral coefficients were extracted from short segments of participants' baseline speech. Intelligibility gains were statistically modeled, and the predictive power of the baseline speech measures was assessed using cross-validation.

Results

Statistical models could predict the intelligibility gains of speakers they had not been trained on. The automated acoustic features were better able to predict speakers' improvement in the loud condition than the manual measures reported in the companion article.

Conclusions

These acoustic analyses present a promising tool for rapidly assessing treatment options. Automated measures of baseline speech patterns may enable more selective inclusion criteria and stronger group outcomes within treatment studies.

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Information & Authors

Information

Published In

Journal of Speech, Language, and Hearing Research
Volume 60Number 11November 2017
Pages: 3058-3068
PubMed: 29075755

History

  • Received: Dec 15, 2016
  • Revised: Jun 27, 2017
  • Accepted: Jun 27, 2017
  • Published in issue: Nov 9, 2017

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Authors

Affiliations

Annalise R. Fletcher
Department of Communication Disorders, University of Canterbury, Christchurch, New Zealand
Alan A. Wisler
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe
Megan J. McAuliffe
Department of Communication Disorders, University of Canterbury, Christchurch, New Zealand
Kaitlin L. Lansford
School of Communication Science & Disorders, Florida State University, Tallahassee
Julie M. Liss
Department of Speech and Hearing Science, Arizona State University, Tempe

Notes

Disclosure: The authors have declared that no competing interests existed at the time of publication.
Correspondence to Annalise Fletcher: [email protected]
Editor-in-Chief: Krista Wilkinson
Editor: Jeannette Hoit

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