@article{Popoola_Folorunso_Asaolu_Joshua_Oyeyemi_2020, title={Person Identification System from Speech and Laughter Using Machine Learning Algorithms}, volume={25}, url={http://jer.unilag.edu.ng/article/view/1000}, abstractNote={<p><span class="fontstyle0">Automated person identification and authentication is paramount for preclusion of cybercrime, national security and veracity<br>of electoral processes. This is a critical component of Information and Communication Technology (ICT), which is the mainstay<br>for national development. This paper presents the use of speech and laughter of people for person identification with the focus<br>on forensics application where people speak and laugh in between. Features were extracted using the Librosa library in Python<br>programming language via Scientific Python Development Environment (SPYDER) IDE (version 4.1.3) of the Anaconda<br>software. While the Orange software (version 3.25.0) for data-mining was used for training, testing and validation of five<br>standard machine learning algorithms: Neural Networks (NN), Support Vector Machine (SVM), Random Forest (RF), Naïve<br>Bayes (NB) and Logistic Regression (LR). Results showed that the neural networks classifier gave the best accuracy followed<br>by the SVM. There was an average of 17.6% and 14.1% increase in the validation metrics when both speech and laughter were<br>combined as compared to speech and laughter independently respectively. This research area is very useful in forensics<br>especially for recognising criminals in conversation.</span> </p&gt;}, number={2}, journal={Journal of Engineering Research}, author={Popoola, O.P. and Folorunso, C.O. and Asaolu, O.S. and Joshua, J.J. and Oyeyemi, M.D.}, year={2020}, month={Jul.}, pages={173-190} }