Voice disorder discrimination using vowel acoustic measures in female speakers

Author:

Nguyen Duy Duong1,Novakovic Daniel1ORCID,Madill Catherine1

Affiliation:

1. Voice Research Laboratory, Sydney School of Health Sciences, Faculty of Medicine and Health The University of Sydney Sydney NSW Australia

Abstract

AbstractBackgroundSustained vowels are important vocal tasks that have been investigated in discriminating voice disorders using acoustic analysis. To date, no study has combined vowel acoustic measures only that evaluate major aspects of the pathological voice signals in voice disorder discrimination.AimsTo investigate the value of vowel acoustic measures that quantify glottal noise, signal stability, signal periodicity, spectral slope and overall voice quality in discriminating female speakers with and without voice disorders.Methods & ProceduresSustained vowel /ɑ/ samples were extracted from 133 voice‐disordered female patients and 97 non‐voice disordered female speakers and were signal typed prior to analysis. Praat software was used to measure harmonics‐to‐noise ratio (HNR), glottal‐to‐noise excitation ratio (GNE), the standard deviation of fundamental frequency (F0SD) and cepstral peak prominence (CPPp); and the Analysis of Dysphonia in Speech and Voice (ADSV) program was used to measure CPPadsv, low/high spectral ratio (LH) and the cepstral/spectral index of dysphonia (CSID). Outcome measures included sensitivity, specificity, and discrimination accuracy.Outcomes & ResultsAs individual acoustic measures, only spectral‐based measures showed good (CPPadsv) and acceptable (CSID) discrimination results. The HNR, GNE and CPPp measures had acceptable sensitivity but poor or non‐acceptable specificity and discrimination accuracy. Logistic regression models with all Praat measures (F0SD, HNR, GNE, CPPp) plus ADSV measures (CPPadsv, LH or CSID) provided excellent sensitivity, good‐to‐excellent specificity and excellent discrimination accuracy. ROC analysis for all individual measures showed that CPPadsv, CSID, CPPp, GNE and F0SD had the highest area under the curve (AUC) values.Conclusions & ImplicationsA combination of acoustic measures that evaluate the major aspects of vocal dysfunction resulted in good to excellent voice discrimination outcomes. Individual acoustic measures had lower discrimination ability than combined measures. The findings implied that acoustic measures extracted from a prolonged vowel were useful in voice disorder discrimination.WHAT THIS PAPER ADDSWhat is already known on this subject Acoustic measures hold great value in discriminating voice disorders from normal voices. However, no study has evaluated discrimination values of a combination of sustained vowel acoustic measures that quantify additive noise, signal stability, signal periodicity, spectral slope and overall voice quality in single‐gender cohorts. Previous studies have not used signal typing (the classification of the acoustic signals) for time‐based measures, impacting the reliability of discrimination.What this study adds to the existing knowledge This study was the first to implement signal typing to include sustained vowel samples of Types 1 and 2 signals for discrimination statistics. We showed that a combination of vocal acoustic measures using time‐ and spectral‐based extraction from the sustained /ɑ/ vowel evaluating additive noise, signal stability, signal periodicity, spectral slope and overall voice quality resulted in good to excellent sensitivity, specificity and discrimination accuracy. As individual measures, traditional time‐based measures such as HNR had rather limited discrimination values whilst spectral‐based measures provided higher discrimination values. Measures that are sensitive to signal types have low discrimination ability.What are the potential or actual clinical implications of this work? The sustained vowel /ɑ/ is a relevant, universal vocal task for clinical application using acoustic measures to discriminate female speakers with and without voice disorders if signal typing is implemented. Clinical voice assessment using vowels may not be effective if relying solely on time‐based measurements. Spectral‐based measures perform better in voice disorder discrimination given their insensitivity to signal types. The most effective voice disorder discrimination could only be obtained using a combination of acoustic measures that quantify major phenomena in the signals of disordered voices. Using measures extracted from both programs, Praat and ADSV, is useful given that specific settings in a program may impact on discrimination accuracy.

Publisher

Wiley

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3