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Construction of an Anchor and Training Sample Set for Auditory-Perceptual Voice Evaluation With the GRBAS-scale
Annelies Labaere1, Marc De Bodt2, Gwen Van Nuffelen3
1Department of Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Department of Speech Pathology and audiology, Thomas More University of Applied Sciences, Antwerp, Belgium.
Objective:
The aim of this study is to construct a representative and comprehensive set of reliable anchor and training voice samples for all parameters and grades of the GRBAS-scale, in order to develop a standardized online training tool for perceptual voice evaluation.
Methods:
140 voice samples varying from normal to severely dysphonic, consisting of the vowel /a/ and a Dutch standard text, were recorded. Samples were duplicated and added to a data folder in random order. A panel of five highly experienced judges rated GRBAS parameters for each voice sample. Intra- and interrater reliability were determined using intraclass correlation coefficient (ICC) and percentage agreement. Samples with agreement percentages of >0.90 for at least one parameter were qualified as anchor voices, whereas samples with a percentage agreement between 0.70 and 0.80 were considered valid training samples.
Results:
Intrarater reliability was moderate to good for all raters and parameters. Interrater reliability was good for parameter G and moderate for R, B, A and S. 83 samples were found to be valid anchor samples with 90-100% expert agreement for at least one parameter, covering all of the GRBAS parameter/score combinations. An additional 37 samples fulfilled the criteria for adoption in the online tool as a training sample, showing 70-80% agreement for one or more parameters.
Conclusion:
Based on well-defined and stringent selection criteria, this study identifies a substantial number of valid anchor and training voices that qualify for implementation in a standardized multi-level training tool for the auditory-perceptual evaluation of voice by means of the GRBAS scale.
