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Related Experiment Video

Updated: Oct 8, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Objective Assessment of Pathological Voice Using Artificial Intelligence Based on the GRBAS Scale.

Tsuyoshi Kojima¹, Shintaro Fujimura², Koki Hasebe³

¹Department of Otolaryngology, Tenri Hospital, Tenri, Nara, Japan.

Journal of Voice : Official Journal of the Voice Foundation

|January 2, 2022

Summary

Artificial intelligence objectively evaluates voice quality using the GRBAS scale. Both Google

Keywords:

Artificial Intelligence GRBAS Pathological Voice Voice disorder

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Area of Science:

Medical Diagnostics
Artificial Intelligence
Speech Science

Background:

The GRBAS scale assesses auditory perceptions of voice quality but is limited by subjective examiner variability.
Objective assessment methods are needed to improve the reliability of voice quality evaluations.

Purpose of the Study:

To introduce objectivity into the GRBAS scale using artificial intelligence.
To compare the accuracy of AI models developed with Google's TensorFlow and Apple's Core ML for voice assessment.

Main Methods:

A machine learning model was trained using 1,377 vowel samples evaluated with the GRBAS scale.
Two AI models were created using TensorFlow and Apple's Create ML.
The accuracy of both models in classifying pathological voice severity based on the GRBAS scale was examined.

Main Results:

Both AI models objectively evaluated GRBAS scales with statistically significant correlations observed for the 'G' (Grade) and 'B' (Breathiness) parameters.
Direct comparison of absolute accuracy was challenging due to methodological differences between TensorFlow and Create ML.

Conclusions:

AI, particularly Apple's Create ML, offers a user-friendly approach to developing objective voice assessment tools by integrating GRBAS data.
Both TensorFlow and Create ML models demonstrated effective performance, suggesting potential for AI in medical screening and transforming clinical voice diagnostics.