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Emotion reputation from speech alerts is a crucial yet difficult part of human-computer interaction (HCI). Several well-known speech assessment and type processes were employed in the literature on speech emotion reputation (SER) to extract emotions from warnings. Deep learning algorithms have recently been proposed as an alternative to conventional ones for SER. We develop a SER system that is totally based on exclusive classifiers and functions extraction techniques. Features from the speech alerts are utilised to train exclusive classifiers. To identify the broadest feasible appropriate characteristic subset, the feature choice (FS) procedure is performed. A number of device studying paradigms have been used for the emotion-related task. A Recurrent Neural Network (RNN) classifier is used to initially categorise seven feelings. A Recurrent Neural Network (RNN) classifier is used to initially categorise seven feelings. Their outcomes are compared to those obtained using Multivariate Linear Regression (MLR) and Support Vector Machines (SVM) methods, which are often used in the area of spoken audio alert emotion identification. The experimental statistics set requires the use of the Berlin and Spanish databases. This investigation demonstrates that the classifiers for the Berlin database attain an accuracy of 83% after applying Speaker Normalization (SN) and a characteristic selection to the functions. The RNN classifier for datasets that has no SN and no FS obtains a high accuracy of 94%.