The Acoustic Breathiness Index (ABI): A Multivariate Acoustic Model for Breathiness

被引:41
|
作者
Latoszek, Ben Barsties V. [1 ,2 ]
Maryn, Youri [1 ,3 ,4 ]
Gerrits, Ellen [5 ,6 ,7 ]
De Bodt, Marc [1 ,8 ,9 ]
机构
[1] Univ Antwerp, Fac Med & Hlth Sci, Univ Pl 1,2610 WILRIJK, Antwerp, Belgium
[2] HAN Univ Appl Sci, Inst Hlth Studies, Nijmegen, Netherlands
[3] Sint Augustinus Hosp, European Inst ORL, Antwerp, Belgium
[4] Univ Coll Ghent, Fac Educ Hlth & Social Work, Ghent, Belgium
[5] HU Univ Appl Sci Utrecht, Fac Hlth Care, Utrecht, Netherlands
[6] Univ Utrecht, Fac Humanities, Utrecht, Netherlands
[7] Univ Med Ctr Utrecht, Dept Otolaryngol, Utrecht, Netherlands
[8] Antwerp Univ Hosp, Dept Otorhinolaryngol Head & Neck Surg, Antwerp, Belgium
[9] Univ Ghent, Fac Med & Hlth Sci, Ghent, Belgium
关键词
Voice assessment; Voice quality; Breathiness; Acoustic measurement; Auditory-perceptual judgment; VOICE QUALITY INDEX; AUDITORY-PERCEPTUAL EVALUATION; MAXIMUM PHONATION TIME; DYSPHONIA SEVERITY; VOCAL QUALITY; PATHOLOGICAL VOICES; CONTINUOUS SPEECH; GRBAS SCALE; RELIABILITY; JUDGMENTS;
D O I
10.1016/j.jvoice.2016.11.017
中图分类号
R36 [病理学]; R76 [耳鼻咽喉科学];
学科分类号
100104 ; 100213 ;
摘要
Objective. The evaluation of voice quality is a major component of voice assessment. The aim of the present study was to develop a new multivariate acoustic model for the evaluation of breathiness. Method. Concatenated voice samples of continuous speech and the sustained vowel [a:] from 970 subjects with dysphonia and 88 vocally healthy subjects were perceptually judged for breathiness severity. Acoustic analyses were conducted on the same concatenated voice samples after removal of the non-voiced segments of the continuous speech sample. The development of an acoustic model for brcathiness was based on stepwise multiple linear regression analysis. Concurrent validity, diagnostic accuracy, and cross validation were statistically verified on the basis of the Spearman rank order correlation coefficient (r(s)), several estimates of the receiver operating characteristics plus the likelihood ratio, and iterated internal cross correlations. Results. Ratings of breathiness from four experts with moderate reliability were used. Stepwise multiple regression analysis yielded a nine-variable acoustic model for the multiparametric measurement of breathiness (Acoustic Breathiness Index [ABI]). A strong correlation was found between ABI and auditory-perceptual rating (r(s) = 0,840, P = 0,000). The cross correlations confirmed a comparably high degree of association, Additionally, the receiver operating characteristics and likelihood ratio results showed the best diagnostic outcome at a threshold of ABI = 3.44 with a sensitivity of 82.4% and a specificity of 92.9%. Conclusions. This study developed a new acoustic multivariate correlate for the evaluation of breathiness in voice. The ABI model showed valid and robust results and is therefore proposed as a new acoustic index for the evaluation of breathiness.
引用
收藏
页码:511.e11 / 511.e27
页数:17
相关论文
共 50 条
  • [41] ACOUSTIC WINDOW MODEL
    KILPATRICK, JF
    JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 1977, 62 : S58 - S58
  • [42] Urban Acoustic Environments - An Acoustic Model for Total Distraction Coefficient
    Suhanek, Mia
    Djurek, Ivan
    Grubesa, Sanja
    Petosic, Antonio
    ACTA ACUSTICA UNITED WITH ACUSTICA, 2019, 105 (02) : 334 - 342
  • [43] Theoretical model of lossy acoustic bipolar cylindrical cloak with negative index metamaterial
    Lee, Yong Y.
    Ahn, Doyeol
    JAPANESE JOURNAL OF APPLIED PHYSICS, 2017, 56 (09)
  • [44] Acoustic wave network and multivariate analysis for biosensing in space
    Christine N. Jayarajah
    Michael Thompson
    Microgravity - Science and Technology, 2005, 16 : 348 - 352
  • [45] Acoustic Wave Network and Multivariate Analysis For Biosensing in Space
    Jayarajah, Christine N.
    Thompson, Michael
    MICROGRAVITY SCIENCE AND TECHNOLOGY, 2005, 16 (1-4) : 348 - 352
  • [46] "Percolation" of acoustic wave in acoustic waveguide composed of two zero-index mediums
    Zhang, Yu-Feng
    Lin, Ji-Zi
    Zhao, Yue
    RESULTS IN PHYSICS, 2021, 27
  • [47] Comparison of Two Multiparameter Acoustic Indices of Dysphonia Severity: The Acoustic Voice Quality Index and Cepstral Spectral Index of Dysphonia
    Lee, Jeong Min
    Roy, Nelson
    Peterson, Elizabeth
    Merrill, Ray M.
    JOURNAL OF VOICE, 2018, 32 (04) : 515.e1 - 515.e13
  • [48] Multivariate Prediction Model of Strength and Acoustic Emission Energy considering Parameter Correlation of Coal or Rock
    Li, Shuncai
    Li, Daquan
    Zhang, Nong
    ADVANCES IN MATERIALS SCIENCE AND ENGINEERING, 2020, 2020
  • [49] NOTE ON THE APPLICATIONS OF A SIMPLE ACOUSTIC IMMERSION INDEX
    Hough, Cameron M.
    ACOUSTICS AUSTRALIA, 2010, 38 (02) : 87 - 93
  • [50] Size and proliferative index correlation in acoustic neuromas
    de Tella, OI
    Stavale, JN
    Herculano, MA
    Net, MAD
    Onishi, FJ
    Guimaraes, FDV
    Silva, LRFE
    ARQUIVOS DE NEURO-PSIQUIATRIA, 2006, 64 (01) : 72 - 76