ArticleFolia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP)2025
Screening Voice Disorders: Acoustic Voice Quality Index, Cepstral Peak Prominence, and Machine Learning.
Article in Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Acoustic Markers of Laryngeal Function and Swallowing Safety in Community-Dwelling Older Adults: An Exploratory Study.Journal of voice : official journal of the Voice Foundation · 2026Article
- SHAP-Based Identification of Potential Acoustic Biomarkers in Patients with Post-Thyroidectomy Voice Disorder.Diagnostics (Basel, Switzerland) · 2025Article
- On Acoustic Voice Quality Index measurement reliability in digital health applications: a narrative review and empirical evaluation of speech sample length requirements.Frontiers in digital health · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
introductionThe Acoustic Voice Quality Index (AVQI) and Smoothed Cepstral Peak Prominence (CPPs) have been reported to effectively support the assessment of voice quality in persons seeking voice care across many languages. This study aimed to evaluate the diagnostic accuracy of these two measures in detecting voice disorders in American English speakers, comparing their performance to machine learning (ML) models.
methodsThis retrospective study included a cohort of 187 participants: 138 patients with clinically diagnosed voice disorders and 49 vocally healthy individuals. Each participant completed two voicing tasks: sustaining [a:] vowel and producing a running speech sample, which were then concatenated. These samples were analyzed using VOXplot software for AVQI-3 (version 03.01) and CPPs. Additionally, four ML models (random forest, k-nearest neighbors, support vector machine, and decision tree) were trained for comparison. The diagnostic accuracy of the two measures and models was assessed using various evaluation metrics, including receiver operating characteristic curve and Youden Index.
resultsA cutoff score of 1.54 for the AVQI-3 (with 55% sensitivity and 80% specificity) and 14.35 dB for CPPs (with 65% sensitivity and 78% specificity) were identified for detecting voice disorders. Compared to an average ML sensitivity of 89% and specificity of 55%, CPPs offered a better balance between sensitivity and specificity, outperforming AVQI-3 and nearly matching the average ML performance.
conclusionsML shows great potential for supporting voice disorder diagnostics, especially as models become more generalizable and easier to interpret. However, current tools like AVQI-3 and CPPs remain more practical and accessible for clinical use in evaluating voice quality than commonly implemented models. CPPs, in particular, offers distinct advantages for identifying voice disorders, making it a recommended and feasible choice for clinics with limited resources.
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