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Convolutional Neural Networks for Pathological Voice Detection.

Huiyi Wu, John Soraghan, Anja Lowit

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 17, 2018

Summary

This summary is machine-generated.

This study introduces a novel Convolutional Neural Network (CNN) approach for classifying pathological and healthy voices using voice spectrograms. The method shows promise for effectively screening voice disorders.

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Published on: July 22, 2025

Area of Science:

Medical acoustics
Computational linguistics
Machine learning for healthcare

Background:

Acoustic analysis and signal processing are vital for extracting voice features.
Distinguishing between pathological and healthy voices is crucial for diagnosis.

Purpose of the Study:

To develop an automated system for voice disorder classification.
To evaluate the effectiveness of a Convolutional Neural Network (CNN) using voice spectrograms.

Main Methods:

Utilized voice recordings from the Saarbruecken Voice Database.
Applied signal processing to generate spectrograms from audio data.
Input spectrograms into a CNN for automatic feature extraction and classification.

Main Results:

Achieved 88.5% accuracy on the training dataset.
Attained 66.2% accuracy on the validation dataset.
Reached 77.0% accuracy on the testing dataset with 482 normal and 482 organic dysphonia speech files.

Conclusions:

The proposed CNN-based algorithm effectively screens pathological voice recordings.
Spectrograms as input to CNNs offer a viable method for voice disorder detection.
This approach demonstrates potential for clinical application in voice pathology screening.