Screening Voice Disorders: Acoustic Voice Quality Index, Cepstral Peak Prominence, and Machine Learning
ML 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.
Authors
- Adrián Castillo‐Allendes (ORCID: https://orcid.org/0000-0002-9511-6203)
- Mark L. Berardi
- Ahmed M. Yousef (ORCID: https://orcid.org/0000-0003-4038-1138)
- Eric J. Hunter
- Juliana Codino (ORCID: https://orcid.org/0000-0002-9616-3378)
- Adam D. Rubin (ORCID: https://orcid.org/0000-0002-6571-7307)
Institutions
- University of Iowa (US)
- Harvard University (US)
- Massachusetts General Hospital (US)
- Lakeshore Foundation (US)
- Michigan State University (US)
Publication Details
- Journal
- Folia Phoniatrica et Logopaedica
- Published
- 2025-02-21
- DOI
- https://doi.org/10.1159/000544852
- Citations
- 3
- Primary Topic
- Voice and Speech Disorders
- Type
- article
- Field-Weighted Citation Impact
- 3.49