Abstract
Abstract: Emotional state identification based on analysis of vocalisations is a challenging subject in the field of HumanComputer Interaction (HCI). In the research that has been done on speech emotion recognition (SER). A wide range of research approaches has been used in order to extract feelings from a variety of inputs, including a number of well- known ways to speech analysis and categorization that are already known. Recent research has suggested the use of deep learning algorithms. as potential alternatives to the approaches that are traditionally used in SER. This article offers a summary of more in- depth topics learning methodologies, as well as current research employing it, are discussed to identify the feelings conveyed by verbal expressions. The analysis will consider the feelings that were recorded in the databases that were utilised were: the contributions to both speech and emotion that were removed the restrictions that were found, as well as the discoveries that were made discovered. Keywords: Speech emotions, Real-time Speech Classification, Transfer Learning, HCI Bandwidth Reduction, SER, LSTM
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