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As the importance of human-computer interaction (HCI) continues to strengthen and the field of deep learning evolves, numerous models have found their application in the realm of Speech Emotion Recognition (SER), leading to significant advancements in recent years. However, effectively recognizing and processing human emotions through computational systems remains a complex and formidable challenge. This review aims to provide a comprehensive summary of the latest accomplishments in SER, encompassing a diverse range of application scenarios, from education and healthcare to criminal investigation. Additionally, it delves into various models and preprocessing techniques such as Convolutional Neural Networks (CNN), Convolutional Recurrent Neural Networks (CRNN), Long Short-Term Memory (LSTM), and datasets like RAVDESS and RECOLA, which encompass a wide array of scenes and languages. While the recent strides in SER have undeniably achieved impressive accuracy rates, a notable gap exists in research that addresses more intricate emotional contexts, including situations involving irony or sarcasm. Consequently, this review focuses on a comprehensive analysis of the limitations inherent in different feature engineering strategies. Moreover, it investigates the challenge of interpretability posed by complex models, the constraint posed by singular and hard-to-gather datasets, and the expansive scope of potential applications SER could serve. Considering these complexities, a potential pathway to further enhance SER's effectiveness and applicability is proposed. This involves exploring the concept of non-binary emotion classification, harnessing rich contextual information, and integrating datasets that incorporate gesture and textual data. By adapting feature extraction techniques to align with the unique demands of specific scenarios, the performance of SER models could be markedly improved.
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