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Screening Voice Disorders: Acoustic Voice Quality Index, Cepstral Peak Prominence, and Machine Learning.

Ahmed M Yousef, Adrián Castillo-Allendes1,2,3, Mark L Berardi3,4

  • 1Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston, Massachusetts, USA.

Folia Phoniatrica Et Logopaedica : Official Organ of the International Association of Logopedics and Phoniatrics (IALP)
|February 23, 2025
PubMed
Summary

Smoothed Cepstral Peak Prominence (CPPs) effectively detects voice disorders in American English speakers, offering a practical clinical tool. While machine learning shows potential, CPPs provide a balanced and accessible approach for voice quality assessment.

Keywords:
Acoustic Voice Quality IndexCepstral Peak ProminenceMachine learningSpeech acousticsVoice disorders

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Area of Science:

  • Speech and Hearing Sciences
  • Medical Acoustics
  • Computational Linguistics

Background:

  • Acoustic Voice Quality Index (AVQI) and Smoothed Cepstral Peak Prominence (CPPs) are established measures for voice quality assessment.
  • Their diagnostic accuracy in detecting voice disorders in American English speakers requires further evaluation.
  • Comparison with machine learning (ML) models is essential for understanding their clinical utility.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of AVQI-3 and CPPs in identifying voice disorders in American English speakers.
  • To compare the performance of AVQI-3 and CPPs against various machine learning models.
  • To determine the most practical and effective measure for clinical voice quality assessment.

Main Methods:

  • Retrospective study of 187 participants (138 with voice disorders, 49 healthy).
  • Analysis of sustained vowel and running speech samples using VOXplot software for AVQI-3 and CPPs.
  • Training and comparison of four ML models (random forest, k-NN, SVM, decision tree) using ROC curve and Youden Index.

Main Results:

  • Optimal cutoff scores identified: AVQI-3 at 1.54 (55% sensitivity, 80% specificity) and CPPs at 14.35 dB (65% sensitivity, 78% specificity).
  • CPPs demonstrated a superior balance of sensitivity and specificity compared to AVQI-3.
  • CPPs performance closely matched the average performance of ML models, outperforming AVQI-3.

Conclusions:

  • Machine learning models show promise for voice disorder diagnostics but require further development for generalizability and interpretability.
  • AVQI-3 and CPPs remain practical and accessible tools for clinical voice quality evaluation.
  • CPPs offer significant advantages for voice disorder identification, making it a recommended choice for resource-limited clinics.