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.
The results vary greatly between the published papers mainly due to differences between sets of data that were used for the experiment. Mart ' i in @cite_21 reported 72 ,
@cite_21
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The paper presents a set of experiments on pathological voice detection over the Saarbrucken Voice Database (SVD) by using the MultiFocal toolkit for a discriminative calibration and fusion. The SVD is freely available online containing a collection of voice recordings of different pathologies, including both functional and organic. A generative Gaussian mixture model trained with mel-frequency cepstral coefficients, harmonics-to-noise ratio, normalized noise energy and glottal-to-noise excitation ratio, is used as classifier. Scores are calibrated to increase performance at the desired operating point. Finally, the fusion of different recordings for each speaker, in which vowels a , i and u are pronounced with normal, low, high, and low-high-low intonations, offers a great increase in the performance. Results are compared with the Massachusetts Eye and Ear Infirmary (MEEI) database, which makes possible to see that SVD is much more challenging.