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Enhanced Living by Assessing Voice Pathology Using a Co-Occurrence Matrix
Ghulam Muhammad1, Mohammed F Alhamid2, M Shamim Hossain3
1Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia. ghulam@ksu.edu.sa.
Sensors (Basel, Switzerland)
|February 2, 2017
Summary
This study introduces a smart home voice pathology assessment system using sensor data and machine learning. The system accurately identifies voice disorders, enhancing life for individuals with disabilities in enhanced living environments (ELEs).
Area of Science:
- Biomedical Engineering
- Artificial Intelligence
- Assistive Technology
Background:
- Millions worldwide live with disabilities, impacting daily life.
- Smart technology offers potential for improved living environments for disabled individuals.
- Current voice pathology assessment methods may lack integration into smart home frameworks.
Purpose of the Study:
- To propose an effective voice pathology assessment system within a smart home framework.
- To leverage sensor data and machine learning for disability assistance.
- To enhance the quality of life in enhanced living environments (ELEs).
Main Methods:
- Acquisition and processing of voice and electroglottography (EGG) signals via sensors.
- Generation of co-occurrence matrices from spectrograms.
- Feature extraction (energy, entropy, contrast, homogeneity) and classification using a Gaussian mixture model.
Main Results:
- The proposed system achieved high accuracy and speed in voice pathology assessment.
- Demonstrated feasibility using the Saarbrucken voice database.
- Validated the system's effectiveness for smart home applications.
Conclusions:
- The developed system is effective for voice pathology assessment in smart home settings.
- The approach shows promise for broader applications in assessing other disabilities.
- Smart technology integration can significantly improve life for people with disabilities.
Keywords:
Saarbrucken voice databaseco-occurrence matrix, Gaussian mixture modelenhanced living environmentvoice pathology assessmentMore Related Videos
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