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

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.

Towards robust voice pathology detection

“…More specifically, most researchers have analyzed sustained phonation of the vowel /a/, e.g. [1,43,7,14] due to its presence in most databases (language-independent speech task [59]). Some researchers also analyzed a combination of the vowels, e.g.…”

“…[36,17,26], etc. From the voice pathologies point of view, most researchers restricted the dataset to a limited set of pathologies [7,43,14,25,51,44,5,3,4,2].…”

“…Next, conventional and clinically interpretable [10]) acoustic features were usually computed prior to pathology detection [43,14,51]. The acoustic features such as multidimensional voice program parameters (MDVP) [4], mel-frequency cepstral coefficients (MFCC) [52], glottalto-noise excitation ratio (GNE) [42], etc.…”

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

“…A total of 83% of the scientific papers selected and summarized in Table S8, presented in the Supplementary Materials file, focus their research exclusively on smart homes, while the remaining 17% analyze both smart homes and smart buildings in general. In these papers, the authors make use of different types of sensors, including smartphone sensors [88]; electroglottography (EGG) electrodes [88]; smart meters [35,87]; wearable sensors providing inertial data, environment sensors and data processed video streams [89]; electricity, water and natural gas consumption sensors [90]; and multi-appliance recognition systems, designing a single smart meter using a current sensor and a voltage sensor in combination with a microprocessor to meter multi-appliances [64].…”

“…With respect to the reasons for implementing the GPR integrated with sensor devices in smart buildings, these are mainly related to human activity recognition/monitoring [35,[87][88][89]; voice pathology assessment [88]; monitoring of human health [89]; ambient assisted living [35]; recognizing household appliances in order to assess their usage and develop habits of power preservation [64]; and developing a framework for automatic leakage detection in smart water and gas grids [90].…”