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Integrating voice evaluation: correlation between acoustic and audio-perceptual measures
Susana Vaz Freitas1, Pedro Melo Pestana2, Vítor Almeida3
1Serviço de Otorrinolaringologia, Centro Hospitalar do Porto, Largo Prof. Abel Salazar, Porto, Portugal; Speech Therapy Department-Faculty of Health Sciences, Universidade Fernando Pessoa, Porto, Portugal; Biomedical Engineering Department-Faculty of Engineering, Universidade do Porto, Porto, Portugal.
Journal of Voice : Official Journal of the Voice Foundation
|January 27, 2015
Summary
Acoustic voice analysis measures show moderate correlations with audio-perceptual ratings using the GRBAS scale. Breathiness (B) had the strongest correlation, though predictive values varied across four voice analysis software programs.
Area of Science:
- Speech science
- Acoustic analysis
- Voice disorders
Background:
- The GRBAS scale is a standard for audio-perceptual voice assessment.
- Acoustic measures offer objective data but their correlation with perceptual ratings needs clarification.
- Variability in voice analysis software may impact results.
Purpose of the Study:
- To correlate acoustic and audio-perceptual measures (GRBAS) across four voice analysis software.
- To identify acoustic predictors for GRBAS parameters.
- To assess software-specific differences in these correlations.
Main Methods:
- Exploratory, transversal study design.
- Analysis of 90 voice recordings using Dr. Speech, Multidimensional Voice Program, PRAAT, and Voice Studio.
- Correlation of acoustic measures with expert-rated GRBAS parameters.
Main Results:
- Predictive values ranged from weak (R(2)a=0.17) to moderate (R(2)a=0.71).
- Breathiness (B) showed the highest correlation magnitude.
- Asthenia (A) and Strain (S) had weaker correlations. Local Shimmer, HNR, APQ5, and PPQ5 jitter were strong predictors, varying by software.
Conclusions:
- Specific acoustic measures predict GRBAS parameters, but vary by software.
- Breathiness (B) demonstrated the strongest acoustic-perceptual correlation.
- Software choice influences the predictive power of acoustic voice analysis.
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