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13 pages, 1100 KiB

Open AccessArticle

Voice Pathology Detection and Classification Using Convolutional Neural Network Model

by Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mohd Khanapi Abd Ghani, Mashael S. Maashi, Begonya Garcia-Zapirain, Ibon Oleagordia, Hosam Alhakami and Fahad Taha AL-Dhief

Appl. Sci. 2020, 10(11), 3723; https://doi.org/10.3390/app10113723 - 27 May 2020

Cited by 167 | Viewed by 12937

Abstract

Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy. Full article

(This article belongs to the Special Issue Medical Artificial Intelligence)

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12 pages, 3597 KiB

Open AccessArticle

Enhanced Living by Assessing Voice Pathology Using a Co-Occurrence Matrix

by Ghulam Muhammad, Mohammed F. Alhamid, M. Shamim Hossain, Ahmad S. Almogren and Athanasios V. Vasilakos

Sensors 2017, 17(2), 267; https://doi.org/10.3390/s17020267 - 29 Jan 2017

Cited by 38 | Viewed by 5547

Abstract

A large number of the population around the world suffers from various disabilities. Disabilities affect not only children but also adults of different professions. Smart technology can assist the disabled population and lead to a comfortable life in an enhanced living environment (ELE). In this paper, we propose an effective voice pathology assessment system that works in a smart home framework. The proposed system takes input from various sensors, and processes the acquired voice signals and electroglottography (EGG) signals. Co-occurrence matrices in different directions and neighborhoods from the spectrograms of these signals were obtained. Several features such as energy, entropy, contrast, and homogeneity from these matrices were calculated and fed into a Gaussian mixture model-based classifier. Experiments were performed with a publicly available database, namely, the Saarbrucken voice database. The results demonstrate the feasibility of the proposed system in light of its high accuracy and speed. The proposed system can be extended to assess other disabilities in an ELE. Full article

(This article belongs to the Special Issue Multisensory Big Data Analytics for Enhanced Living Environments)

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