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DOI: 10.14569/IJACSA.2020.0111082
PDF

Static vs. Dynamic Modelling of Acoustic Speech Features for Detection of Dementia

Author 1: Muhammad Shehram Shah Syed
Author 2: Zafi Sherhan Syed
Author 3: Elena Pirogova
Author 4: Margaret Lech

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 10, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Dementia is a chronic neurological disease that causes cognitive disabilities and significantly impacts daily ac-tivities of affected individuals. It is known that early detection of dementia can improve the quality of life of patients through a specialized care program. Recently, there has been a growing interest in speech-based screening of neurological diseases such as dementia. The focus is on continuous monitoring of changes in speech of dementia patients, aiming to identify the early onset of the disease which could facilitate development of preventative treatment care. In this work, we propose a dynamic (temporal) modeling of acoustic speech characteristics aiming at identifying the signs of dementia. The classification performance of the proposed framework is compared with a baseline static modeling of acoustic speech features. Experimental results show that the proposed dynamic approach outperforms the static method. It achieves the classification accuracy of 74.55% compared to 66.92% obtained using the static models.

Keywords: Dementia detection; speech classification; neural networks; recurrent neural networks

Muhammad Shehram Shah Syed, Zafi Sherhan Syed, Elena Pirogova and Margaret Lech, “Static vs. Dynamic Modelling of Acoustic Speech Features for Detection of Dementia” International Journal of Advanced Computer Science and Applications(IJACSA), 11(10), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111082

@article{Syed2020,
title = {Static vs. Dynamic Modelling of Acoustic Speech Features for Detection of Dementia},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111082},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111082},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {10},
author = {Muhammad Shehram Shah Syed and Zafi Sherhan Syed and Elena Pirogova and Margaret Lech}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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