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Related Experiment Video

Updated: Jun 16, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Published on: August 9, 2024

Developing a smart system for binary classification of disordered voices using machine learning.

Yat Chun Au¹, Manwa L Ng¹

¹Speech Science Laboratory, Faculty of Education, 729 Meng Wah Complex, University of Hong Kong, Hong Kong, China.

American Journal of Otolaryngology

|May 17, 2025

Summary

This summary is machine-generated.

Machine learning models, specifically Random Forest (RF), accurately classify voice disorders using acoustic features. This automated analysis offers a reliable, non-invasive method for early detection and improved patient outcomes in voice care.

Keywords:

Acoustic analysis Machine learning Voice classification Voice disorders Voice pathology

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Last Updated: Jun 16, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Published on: August 9, 2024

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Published on: September 27, 2024

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Area of Science:

Speech and Hearing Sciences
Computational Linguistics
Biomedical Engineering

Background:

Voice disorders stem from disrupted vocal fold vibration during phonation, impacting voice quality.
Accurate diagnosis is crucial for effective management and improved patient outcomes.
Traditional diagnostic methods can be subjective and time-consuming.

Purpose of the Study:

To explore the application of machine learning (Random Forest and Decision Tree models) for classifying normophonic and disordered voices.
To compare the diagnostic utility of RF and DT classifiers using acoustic features.
To evaluate individual acoustic parameters across multilingual databases, focusing on Cantonese voice samples.

Main Methods:

Utilized 1986 sustained vowel /a/ recordings from three databases, including a local Cantonese clinical repository.
Extracted 29 acoustic features using Parselmouth (Python interface to Praat).
Trained and validated RF and DT models, comparing performance via accuracy, sensitivity, specificity, and F1-score; assessed feature importance and performed ROC analysis.

Main Results:

The Random Forest (RF) model achieved 89% accuracy, outperforming the Decision Tree (DT) model (78% accuracy).
Key acoustic features for classification included age, CSID, shimmer, and jitter.
CSID and stdevF0Hz were reliable discriminators for males, while CSID, localabsoluteJitter, apq11Shimmer, and localdbShimmer were effective for females, though population-specific calibration is needed.

Conclusions:

Machine learning, particularly the RF algorithm, significantly enhances voice disorder diagnosis accuracy through automated acoustic feature analysis.
Integrating ML models offers reliable, non-invasive methods for early detection and management, potentially improving patient outcomes.
Further research should focus on dataset diversity and validation to improve generalizability and clinical applicability.