Speech Recognition for COVID-19 Keywords Using Machine Learning

Authors

  • Wael Ben Amara Mediterranean Institute of Technology, South Mediterranean University, Les Berges du Lac II, 1053 Tunisia
  • Amani Touihri Mediterranean Institute of Technology, South Mediterranean University, Les Berges du Lac II, 1053 Tunisia
  • Salma Hamza Mediterranean Institute of Technology, South Mediterranean University, Les Berges du Lac II, 1053 Tunisia

Keywords:

COVID-19, Support Vector Machine, Artificial Neural Network

Abstract

As of June 01, 2020, coronavirus disease, 2019 (COVID-19) has been confirmed in 7,274,000 people worldwide, affecting over 213 countries. It becomes a major healthcare challenge around the world to counter this novel epidemic. The aim of this study is to investigate the detection of patients with suspected COVID-19 infection through phone calls. Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers for detection to COVID patients are built and their performances are compared. Experiments were carried out on Arabic speech signals of recorded phone calls. From speech signals, relevant feature extraction of keywords is achieved. The results are very promising. We have reached 97% accuracy. Thanks to this classification, we would be able to know if the recorded call deserves a callback or not which would ease the workload on the health care system. The model can evolve by building better and more solid classifiers that can be used in public security when it comes to analyzing phone calls

 

References

A. S. S. Rao, and J. A. Vazquez, ?Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey in the populations when cities/towns are under quarantine," Infection Control & Hospital Epidemiology, vol.41 no 7, pp. 826-830, 2020.

O. Abdel-Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, and Y. Dong, ?Convolutional neural networks for speech recognition,? Journal of IEEE/ACM Transactions on audio, speech, and language processing, Vol.22, pp.1533-1545, 2014.

J. Samuel, G.G. Ali, M Rahman, E. Esawi, Y, ?Covid-19 public sentiment insights and machine learning for tweets classification,? Information, vol. 11, no 6, pp. 314, 2020.

R. Vaishya, M. Javaid, I. H. Khan et A. Haleem, ?Artificial Intelligence (AI) applications for COVID-19 pandemic,? Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2020.

S. K. Gaikwad, B. W. Gawali, and P. Yannawar, ?A review on speech recognition technique,? International Journal of Computer Applications, vol. 10, no 3, pp. 16-24, 2010.

H. Bouhamed, ?Covid-19 cases and recovery previsions with deep learning nested sequence prediction models with long short-term memory (LSTM) architecture,? Int. J. Sci. Res. in Computer Science and Engineering, vol. 8, no 2, 2020.

L. Yan, H. Zhang, Y. Xiao, et al. ?Prediction of survival for severe Covid-19 patients with three clinical features: development of a machine learning-based prognostic model with clinical data in Wuhan,? medRxiv, 2020.

Z. I. Khan, Y. Javed, and K.N. Shmasi ?Correlation study of New Cases, Deaths, Recoveries and Temperature with Machine Learning during COVID-19 spread in Saudi Arabia,? Int. J. Sci. Res. in Computer Science and Engineering, vol. 8, no 3, 2020.

B.W. Schuller, D.M. Schuller, K. Qian, J. Liu, H. Zheng, and X. Li ?Covid-19 and computer audition: An overview on what speech & sound analysis could contribute in the SARS-CoV-2 Corona crisis,? arXiv preprint arXiv:2003. 11117, 2020.

E. Guresen, and G. Kayakutlu, ?Definition of artificial neural networks with comparison to other networks,? Procedia Computer Science, vol. 3, pp. 426-433, 2011.

M.M. Mijwel, ?Artificial neural networks advantages and disadvantages,? Retrieved from LinkedIn: https://www. linkedin. com/pulse/artificial-neural-net works-advantages-disadvantages-maad-m-mijwel, 2018.

C. Campbell, and Y. Ying. ?Learning with support vector machines,? Synthesis lectures on artificial intelligence and machine learning, vol. 5, no. 1, pp. 1-95, 2011.

P. Virtanen, R. Gommers, T.E. Oliphant, Haberland, M. Haberland, T. Reddy, D. Cournapeau, ... & van der Walt, S. J. SciPy 1.0: ?fundamental algorithms for scientific computing in Python. Nature methods,? vol. 17, no 3, pp. 261-272, 2020.

D. P. Kingma, and Ba. Jimmy ?Adam: A method for stochastic optimization,? arXiv preprint arXiv:1412.6980, 2014.

N. Cristianini, and J. Shawe-Taylor. ?An introduction to support vector machines and other kernel-based learning methods,? Cambridge university press, 2000.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Vanderplas, J. ?Scikit-learn: Machine learning in Python,? the Journal of machine Learning research, vol. 12, p. 2825-2830, 2011.

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Published

2020-08-31

How to Cite

[1]
W. B. Amara, A. Touihri, and S. Hamza, “Speech Recognition for COVID-19 Keywords Using Machine Learning”, Int. J. Sci. Res. Comp. Sci. Eng., vol. 8, no. 4, pp. 51–57, Aug. 2020.

Issue

Vol. 8 No. 4 (2020): August

Section

Research Article

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