Abstract
. Speech Emotion Recognition (SER) is an interdisciplinary field that leverages signal processing and machine learning techniques to identify and classify emotions conveyed through speech. In recent years, SER has gained significant attention due to its potential applications in human-computer interaction, healthcare, education, and customer service. Emotions such as happiness, anger, sadness, fear, surprise, and disgust can be inferred from various acoustic features including pitch, intensity, speech rate, and spectral characteristics. However, accurately recognizing emotions from speech is challenging due to factors such as speaker variability, cultural differences, background noise, and the subtleties of emotional expression. This paper explores the state-of-the-art methodologies for speech emotion recognition, with an emphasis on deep learning approaches, feature extraction techniques, and the use of large-scale emotion-labeled datasets. We review traditional approaches, such as hidden Markov models and support vector machines, and compare them with modern advancements in neural networks, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Additionally, we discuss the challenges in the field, including emotion detection in spontaneous speech, the impact of cross-lingual and crosscultural recognition, and the limitations of current benchmarks. Finally, we provide an overview of real-world applications of SER systems, including their integration into virtual assistants, mental health diagnostics, and interactive entertainment. We conclude by highlighting emerging trends in multimodal emotion recognition and the potential for future research in improving the robustness and accuracy of SER systems in diverse environments.