Discriminating Dysarthria Type From Envelope Modulation Spectra PurposePrevious research demonstrated the ability of temporally based rhythm metrics to distinguish among dysarthrias with different prosodic deficit profiles (J. M. Liss et al., 2009). The authors examined whether comparable results could be obtained by an automated analysis of speech envelope modulation spectra (EMS), which quantifies the rhythmicity of speech ... Research Note
Research Note  |   October 01, 2010
Discriminating Dysarthria Type From Envelope Modulation Spectra
 
Author Affiliations & Notes
  • Julie M. Liss
    Arizona State University, Tempe
  • Sue LeGendre
    University of Arizona, Tucson
  • Andrew J. Lotto
    University of Arizona, Tucson
  • Contact author: Julie M. Liss, Motor Speech Disorders Laboratory, Arizona State University Coor, 870102, Tempe, AZ 85287. E-mail: julie.liss@asu.edu.
Article Information
Speech, Voice & Prosodic Disorders / Dysarthria / Speech, Voice & Prosody / Speech
Research Note   |   October 01, 2010
Discriminating Dysarthria Type From Envelope Modulation Spectra
Journal of Speech, Language, and Hearing Research, October 2010, Vol. 53, 1246-1255. doi:10.1044/1092-4388(2010/09-0121)
History: Received June 18, 2009 , Accepted February 18, 2010
 
Journal of Speech, Language, and Hearing Research, October 2010, Vol. 53, 1246-1255. doi:10.1044/1092-4388(2010/09-0121)
History: Received June 18, 2009; Accepted February 18, 2010
Web of Science® Times Cited: 26

PurposePrevious research demonstrated the ability of temporally based rhythm metrics to distinguish among dysarthrias with different prosodic deficit profiles (J. M. Liss et al., 2009). The authors examined whether comparable results could be obtained by an automated analysis of speech envelope modulation spectra (EMS), which quantifies the rhythmicity of speech within specified frequency bands.

MethodEMS was conducted on sentences produced by 43 speakers with 1 of 4 types of dysarthria and healthy controls. The EMS consisted of the spectra of the slow-rate (up to 10 Hz) amplitude modulations of the full signal and 7 octave bands ranging in center frequency from 125 to 8000 Hz. Six variables were calculated for each band relating to peak frequency and amplitude and relative energy above, below, and in the region of 4 Hz. Discriminant function analyses (DFA) determined which sets of predictor variables best discriminated between and among groups.

ResultsEach of 6 DFAs identified 2–6 of the 48 predictor variables. These variables achieved 84%–100% classification accuracy for group membership.

ConclusionsDysarthrias can be characterized by quantifiable temporal patterns in acoustic output. Because EMS analysis is automated and requires no editing or linguistic assumptions, it shows promise as a clinical and research tool.

Acknowledgments
This work was supported by a research grant from the National Institute on Deafness and Other Communication Disorders (Grant 5 R01 DC 6859) to the first author and (Grant 5 R01 DC 4674) to the third author. The authors are especially appreciative for those who supported and participated in this research, including volunteers from Mayo Clinic Arizona; Phil Hardt and members of the Huntington’s Disease Society of America, Arizona Affiliate; the Arizona Ataxia Support Group; Pamela Mathy; and K. Sivakumar and his patients at the Neuromuscular Research Center, Scottsdale, Arizona.
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