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Detecting of Voice Fatigue With Artificial Intelligence.

Abhinav Siripurapu¹, Robert T Sataloff²

¹The Hill School, Pottstown, Pennsylvania.

Journal of Voice : Official Journal of the Voice Foundation

|August 25, 2024

Summary

This summary is machine-generated.

This study introduces an AI system for detecting voice fatigue (VF) with 93% accuracy. The AI model

Keywords:

Voice fatigue—Convolutional Neural Networks—Detection

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Area of Science:

Speech and Hearing Science
Artificial Intelligence in Medicine
Machine Learning for Health

Background:

Voice fatigue (VF) presents diagnostic challenges due to subjective symptoms and difficulty in objective quantification.
Current methods for detecting VF are limited, necessitating novel approaches for accurate assessment.

Purpose of the Study:

To develop and evaluate an AI-based system for the automatic detection and monitoring of voice fatigue.
To compare the performance of the AI model against traditional assessments by speech-language pathologists (SLPs).

Main Methods:

Collected voice samples from individuals with varying levels of VF.
Utilized an ECAPA-TDNN model to extract voice embeddings and a Convolutional Neural Network for classification.
Validated the AI model by comparing its accuracy against assessments from experienced SLPs.

Main Results:

The AI model achieved 93% accuracy in detecting voice fatigue on a dataset of academic lectures and podcasts.
The AI model's classification accuracy was 86% when compared to the ratings of three experienced SLPs.

Conclusions:

AI offers a generalizable and effective approach for the analysis and detection of voice fatigue.
Future research will focus on validating these AI models with patient data for broader clinical application.