ABSTRACT

A method for recognising a speaker based on speech features is known as speaker recognition. Speaker recognition technology is frequently employed in a variety of fields. Most speaker identification algorithms have been developed on regular, clean recordings, but their effectiveness degrades when hearing speech with emotions. This study offers an emotional speech signals system developed using standard machine learning algorithms on an emotional speech database obtained from the University Audio visual Database of Affective Speech and Song using temporal, frequency, and spectral properties Five models (Logistic Regression, Support Vector Machine, Variational Forest, XGBoost, and k-Nearest Neighbor) and three deep learning models (Logistic Regression, Logistic Regression, Random Forest, XGBoost, and k-Nearest Neighbor) were trained and compared in terms of performance (Long Short-Term Memory network, Multilayer Perceptron, and Convolutional Neural Network).Deep neural networks outperformed state-of-the-art models in feelings speaker detection from voice signals after the models were evaluated. They achieved the greatest accuracy of 92 percent, exceeding machine learning techniques. Keywords: Neural networks, machine learning, emotion recognition, speaker recognition
Publication date: 01/03/2023

https://ijbpas.com/pdf/2023/March/MS_IJBPAS_2023_6968.pdf

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https://doi.org/10.31032/IJBPAS/2023/12.3.6968