Person Identification System from Speech and Laughter Using Machine Learning Algorithms

  • O.P. Popoola Department of Systems Engineering, University of Lagos, Nigeria
  • C.O. Folorunso
  • O.S. Asaolu
  • J.J. Joshua
  • M.D. Oyeyemi
Keywords: person identification, laughter, speech, forensics

Abstract

Automated person identification and authentication is paramount for preclusion of cybercrime, national security and veracity
of electoral processes. This is a critical component of Information and Communication Technology (ICT), which is the mainstay
for national development. This paper presents the use of speech and laughter of people for person identification with the focus
on forensics application where people speak and laugh in between. Features were extracted using the Librosa library in Python
programming language via Scientific Python Development Environment (SPYDER) IDE (version 4.1.3) of the Anaconda
software. While the Orange software (version 3.25.0) for data-mining was used for training, testing and validation of five
standard machine learning algorithms: Neural Networks (NN), Support Vector Machine (SVM), Random Forest (RF), Naïve
Bayes (NB) and Logistic Regression (LR). Results showed that the neural networks classifier gave the best accuracy followed
by the SVM. There was an average of 17.6% and 14.1% increase in the validation metrics when both speech and laughter were
combined as compared to speech and laughter independently respectively. This research area is very useful in forensics
especially for recognising criminals in conversation.

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Published
2020-07-27
How to Cite
Popoola, O., Folorunso, C., Asaolu, O., Joshua, J., & Oyeyemi, M. (2020). Person Identification System from Speech and Laughter Using Machine Learning Algorithms. Journal of Engineering Research, 25(2), 173-190. Retrieved from http://jmtcs.unilag.edu.ng/index.php/jer/article/view/1000