Literature DB >> 17902870

When and why listeners disagree in voice quality assessment tasks.

Jody Kreiman1, Bruce R Gerratt, Mika Ito.   

Abstract

Modeling sources of listener variability in voice quality assessment is the first step in developing reliable, valid protocols for measuring quality, and provides insight into the reasons that listeners disagree in their quality assessments. This study examined the adequacy of one such model by quantifying the contributions of four factors to interrater variability: instability of listeners' internal standards for different qualities, difficulties isolating individual attributes in voice patterns, scale resolution, and the magnitude of the attribute being measured. One hundred twenty listeners in six experiments assessed vocal quality in tasks that differed in scale resolution, in the presence/absence of comparison stimuli, and in the extent to which the comparison stimuli (if present) matched the target voices. These factors accounted for 84.2% of the variance in the likelihood that listeners would agree exactly in their assessments. Providing listeners with comparison stimuli that matched the target voices doubled the likelihood that they would agree exactly. Listeners also agreed significantly better when assessing quality on continuous versus six-point scales. These results indicate that interrater variability is an issue of task design, not of listener unreliability.

Mesh:

Year:  2007        PMID: 17902870     DOI: 10.1121/1.2770547

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  26 in total

1.  Developing a single comparison stimulus for matching breathy voice quality.

Authors:  Sona Patel; Rahul Shrivastav; David A Eddins
Journal:  J Speech Lang Hear Res       Date:  2012-01-03       Impact factor: 2.297

2.  Perceptual interaction of the harmonic source and noise in voice.

Authors:  Jody Kreiman; Bruce R Gerratt
Journal:  J Acoust Soc Am       Date:  2012-01       Impact factor: 1.840

3.  Perceptual sensitivity to first harmonic amplitude in the voice source.

Authors:  Jody Kreiman; Bruce R Gerratt
Journal:  J Acoust Soc Am       Date:  2010-10       Impact factor: 1.840

4.  Structural sensing of interior sound for active control of noise in structural-acoustic cavities.

Authors:  Ashok K Bagha; S V Modak
Journal:  J Acoust Soc Am       Date:  2015-07       Impact factor: 1.840

5.  Perceptual evaluation of voice source models.

Authors:  Jody Kreiman; Marc Garellek; Gang Chen; Abeer Alwan; Bruce R Gerratt
Journal:  J Acoust Soc Am       Date:  2015-07       Impact factor: 1.840

6.  Psychometric properties associated with perceived vocal roughness using a matching task.

Authors:  David A Eddins; Rahul Shrivastav
Journal:  J Acoust Soc Am       Date:  2013-10       Impact factor: 1.840

7.  Modal and non-modal voice quality classification using acoustic and electroglottographic features.

Authors:  Michal Borsky; Daryush D Mehta; Jarrad H Van Stan; Jon Gudnason
Journal:  IEEE/ACM Trans Audio Speech Lang Process       Date:  2017-11-27

8.  Concatenation of the Moving Window Technique for Auditory-Perceptual Analysis of Voice Quality.

Authors:  Benjamin Ehrlich; Liyu Lin; Jack Jiang
Journal:  Am J Speech Lang Pathol       Date:  2018-11-21       Impact factor: 2.408

9.  Identifying a comparison for matching rough voice quality.

Authors:  Sona Patel; Rahul Shrivastav; David A Eddins
Journal:  J Speech Lang Hear Res       Date:  2012-02-21       Impact factor: 2.297

10.  Acoustic and perceptual effects of changes in body layer stiffness in symmetric and asymmetric vocal fold models.

Authors:  Zhaoyan Zhang; Jody Kreiman; Bruce R Gerratt; Marc Garellek
Journal:  J Acoust Soc Am       Date:  2013-01       Impact factor: 1.840

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