Emotional responses play a crucial role in daily social interactions, enabling us to perceive and understand others’ moods and feelings. The field of emotion detection and recognition is rapidly evolving, with Speech Emotion Recognition (SER) emerging as a prominent research area. SER involves the analysis and identification of human emotions through speech patterns, offering significant potential applications in human-computer interaction, healthcare, and education. Current systems for emotion recognition from speech signals employ a variety of techniques, including natural language processing, signal processing, and machine learning. These techniques extract relevant features from speech signals and classify them into different emotional categories. Given the rich characteristics of speech, it serves as an excellent resource for computational linguistics. While previous studies have proposed various methods for speech emotion classification, there is a pressing need to enhance the effectiveness of voice-based emotion identification. This is primarily due to the limited knowledge on the fundamental temporal link of the speech waveform. This paper aims to advance speech emotion recognition by uncovering valuable insights through the utilization of signal processing and feature extraction techniques.

Emotional responses, Speech Emotion Recognition (SER), Human-computer interaction, Feature extraction, Natural language processing, Machine learning.

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