How Do Voice Perceptual Changes Predict Acoustic Parameters in Persian Voice Patients?

Area of Science:

• Speech-Language Pathology
• Acoustic Phonetics
• Voice Science

Background:

• Perceptual and acoustic analyses are crucial for voice therapists assessing voice quality.
• Perceptual evaluations are subjective, while acoustic analysis offers objective data.
• Understanding the relationship between perceptual and acoustic voice parameters is vital for accurate diagnosis and treatment.

Purpose of the Study:

• To identify acoustic parameters predicted by perceptual voice quality in Persian speakers.
• To evaluate the impact of targeted training on the reliability of perceptual voice assessments.
• To enhance the diagnostic accuracy of voice quality evaluations.

Main Methods:

• Cross-sectional study involving 20 patients with voice disorders.
• Perceptual voice evaluations using the Grade, Roughness, Breathiness, Asthenia, and Strain (GRBAS) scale by 15 expert clinicians.
• Acoustic analysis using Praat software, with reliability assessed via intraclass correlation coefficient (ICC) and data analyzed using ordinal regression.

Main Results:

• Post-training, both intrarater and interrater reliability significantly improved for all five perceptual parameters, with ICC for grade reaching 0.95.
• Grade and roughness perceptions significantly predicted fundamental frequency (F0) and harmonic-to-noise ratio (HNR).
• Breathiness perception was a significant predictor of shimmer.

Conclusions:

• Training demonstrably enhances the reliability of perceptual voice evaluations.
• For Persian speakers, specific acoustic changes (F0, HNR, shimmer) are perceptually linked to impaired voice quality.
• Integrating acoustic analysis with trained perceptual evaluation can optimize voice disorder assessment.