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Sensors 2017, 17(2), 267; doi:10.3390/s17020267

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

1
Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
2
Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
3
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
4
Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 93187 Skellefteå, Sweden
*
Author to whom correspondence should be addressed.
Academic Editor: Yu Hen Hu
Received: 22 November 2016 / Revised: 14 January 2017 / Accepted: 25 January 2017 / Published: 29 January 2017
(This article belongs to the Special Issue Multisensory Big Data Analytics for Enhanced Living Environments)
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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. View Full-Text
Keywords: voice pathology assessment; enhanced living environment; Saarbrucken voice database; co-occurrence matrix, Gaussian mixture model voice pathology assessment; enhanced living environment; Saarbrucken voice database; co-occurrence matrix, Gaussian mixture model
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MDPI and ACS Style

Muhammad, G.; Alhamid, M.F.; Hossain, M.S.; Almogren, A.S.; Vasilakos, A.V. Enhanced Living by Assessing Voice Pathology Using a Co-Occurrence Matrix. Sensors 2017, 17, 267.

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