Affiliation:
1. School of Communication Science and Disorders, Florida State University, Tallahassee
Abstract
Purpose:
This study sought to determine if alternative vowel space area (VSA) measures (i.e., novel trajectory-based measures: vowel space hull area and vowel space density) predicted speech intelligibility to the same extent as two traditional vowel measures (i.e., token-based measures: VSA and corner dispersion) in speakers with dysarthria. Additionally, this study examined if the strength of the relationship between acoustic vowel measures and intelligibility differed based on how intelligibility was measured (i.e., orthographic transcriptions [OTs] and visual analog scale [VAS] ratings).
Method:
The Grandfather Passage was read aloud by 40 speakers with dysarthria of varying etiologies, including Parkinson's disease (
n
= 10), amyotrophic lateral sclerosis (
n
= 10), Huntington's disease (
n
= 10), and cerebellar ataxia (
n
= 10). Token- and trajectory-based acoustic vowel measures were calculated from the passage. Naïve listeners (
N
= 140) were recruited via crowdsourcing to provide OTs and VAS intelligibility ratings. Hierarchical linear regression models were created to model OTs and VAS intelligibility ratings using the acoustic vowel measures as predictors.
Results:
Traditional VSA was the sole significant predictor of speech intelligibility for both the OTs (
R
2
= .259) and VAS (
R
2
= .236) models. In contrast, the trajectory-based measures were not significant predictors of intelligibility. Additionally, the OTs and VAS intelligibility ratings conveyed similar information.
Conclusions:
The findings suggest that traditional token-based vowel measures better predict intelligibility than trajectory-based measures. Additionally, the findings suggest that VAS methods are comparable to OT methods for estimating speech intelligibility for research purposes.
Publisher
American Speech Language Hearing Association
Subject
Speech and Hearing,Linguistics and Language,Language and Linguistics
Reference48 articles.
1. Berger, D. E. (2004). Using regression analysis. In J. S. Wholey , H. P. Hatry , & K. E. Newcomer (Eds.), Handbook of practical program evaluation (2nd ed., pp. 479–505). Jossey-Bass.
2. Boersma W. &
Weenink D.
(2012). Praat: Doing phonetics by computer (Version 6.2.10) [Computer software]
.
http://www.praat.org/
3. Autoscore: An open-source automated tool for scoring listener perception of speech
4. Predicting Intelligibility Deficits in Parkinson's Disease With Perceptual Speech Ratings
5. Computational Methods for Normalizing Acoustic Vowel Data for Talker Differences
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献