Voice Pathology Detection Using Deep Learning: a Preliminary Study
| Autor: | Harar, Pavol, Alonso-Hernandez, Jesus B., Mekyska, Jiri, Galaz, Zoltan, Burget, Radim, Smekal, Zdenek |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | In 2017 international conference and workshop on bioinspired intelligence (IWOBI), pp. 1-4. IEEE, 2017 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/IWOBI.2017.7985525 |
| Popis: | 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. Comment: 4 pages, 1 figure, 5 tables |
| Databáze: | arXiv |
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