An Evaluation Study for Worthwhile Research in Speech Emotion Recognition Using Deep Learning
DOI:
https://doi.org/10.24237/Keywords:
Speech Emotion Recognition,SRE,Deep Learning, CNN in SER,Hybrid Models in Emotion Detection.Abstract
Speech Emotion Recognition (SER) is an emerging field in Human-Computer Interaction (HCI) focused on detecting emotions from speech. This study reviews recent advancements in SER, emphasizing AI methods, particularly deep learning techniques that significantly enhanced emotion detection accuracy. Sources include Google Scholar, IEEE, ResearchGate, and MDPI, providing an in-depth assessment of evolving techniques. Developments in deep learning, especially Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, improved precision and robustness. Feature extraction techniques such as MFCC and spectrogram analysis, alongside training methods like data augmentation and transfer learning with datasets like RAVDESS and IEMOCAP, are explored. Additionally, hybrid and multimodal approaches combining speech with text and facial expressions are highlighted to enhance recognition accuracy. The paper compares AI models' performance, identifying strengths and areas needing development. This study provides comprehensive insight into current SER progress, summarizes recent techniques, and suggests future research directions to further improve the accuracy and adaptability of SER systems.
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