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.
Highlights
The number of people in the world suffering from different pathologies has increased for several reasons, including pollution, excessive abuse of certain organs, and stress
We propose a smart solution to assess voice pathologies using co-occurrence matrices and a Gaussian mixture model (GMM)
Speakers signals from normal people and people with different voice pathologies
Summary
The number of people in the world suffering from different pathologies has increased for several reasons, including pollution, excessive abuse of certain organs, and stress. These pathologies hinder the normal lives of people; the introduction of smart homes makes these lives easier in many senses. One of the major applications of these smart solutions is healthcare [2]. Some of the healthcare solutions in smart homes have been reported in the literature. A summary work on the development of smart homes in relation to the rehabilitation of neurologically disabled patients can be found in [4]
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