# Voice Pathology Detection Using Deep Learning: a Preliminary Study

arXiv: https://arxiv.org/abs/1907.05905 · HTML: https://mlsanity.com/paper/1907.05905/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection](https://mlsanity.com/paper/2211.09858/) (2022)
- [Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer](https://mlsanity.com/paper/2310.17049/) (2023)
- [Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference Feature](https://mlsanity.com/paper/2410.10537/) (2024)
- [Voice Pathology Detection Using Phonation](https://mlsanity.com/paper/2508.07587/) (2025)
- [Quantifying Quanvolutional Neural Networks Robustness for Speech in Healthcare Applications](https://mlsanity.com/paper/2601.02432/) (2026)

## Abstract (arXiv)

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.