  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.
