TY - JOUR AU - O.P. Popoola AU - C.O. Folorunso AU - O.S. Asaolu AU - J.J. Joshua AU - M.D. Oyeyemi PY - 2020/07/27 Y2 - 2024/03/29 TI - Person Identification System from Speech and Laughter Using Machine Learning Algorithms JF - Journal of Engineering Research JA - JER VL - 25 IS - 2 SE - Articles DO - UR - http://jer.unilag.edu.ng/article/view/1000 AB - Automated person identification and authentication is paramount for preclusion of cybercrime, national security and veracityof electoral processes. This is a critical component of Information and Communication Technology (ICT), which is the mainstayfor national development. This paper presents the use of speech and laughter of people for person identification with the focuson forensics application where people speak and laugh in between. Features were extracted using the Librosa library in Pythonprogramming language via Scientific Python Development Environment (SPYDER) IDE (version 4.1.3) of the Anacondasoftware. While the Orange software (version 3.25.0) for data-mining was used for training, testing and validation of fivestandard machine learning algorithms: Neural Networks (NN), Support Vector Machine (SVM), Random Forest (RF), NaïveBayes (NB) and Logistic Regression (LR). Results showed that the neural networks classifier gave the best accuracy followedby the SVM. There was an average of 17.6% and 14.1% increase in the validation metrics when both speech and laughter werecombined as compared to speech and laughter independently respectively. This research area is very useful in forensicsespecially for recognising criminals in conversation. ER -