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arXiv · 1907.05905

Voice Pathology Detection Using Deep Learning: a Preliminary Study

Pavol Harar·Jesus B. Alonso-Hernandez·Jiri Mekyska·Zoltan Galaz·Radim Burget·Zdenek Smekal
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Abstract

This paper describes a preliminary investigation of Voice Pathology Detection using Deep Neural Networks (DNN). We used voice recordings of sustained vowel /a/ produced at normal pitch from German corpus Saarbruecken Voice Database (SVD). This corpus contains voice recordings and electroglottograph signals of more than 2 000 speakers. The idea behind this experiment is the use of convolutional layers in combination with recurrent Long-Short-Term-Memory (LSTM) layers on raw audio signal. Each recording was split into 64 ms Hamming windowed segments with 30 ms overlap. Our trained model achieved 71.36% accuracy with 65.04% sensitivity and 77.67% specificity on 206 validation files and 68.08% accuracy with 66.75% sensitivity and 77.89% specificity on 874 testing files. This is a promising result in favor of this approach because it is comparable to similar previously published experiment that used different methodology. Further investigation is needed to achieve the state-of-the-art results.

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Pavol Harar, Jesus B. Alonso-Hernandez, Jiri Mekyska, Zoltan Galaz, Radim Burget, Zdenek Smekal. 2019-07-12. Voice Pathology Detection Using Deep Learning: a Preliminary Study. https://doi.org/10.1109/iwobi.2017.7985525

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