Speaker-Dependent Speech Emotion Recognition System

In this paper, we focus on introducing a speaker-dependent speech emotion recognition system. The system utilizes speech signals and emotional features for emotion classification, implemented through Python-based frameworks with Mel-frequency cepstral coefficients (MFCC) feature extraction and support vector machine (SVM) classifiers. Alongside the research paper, we provide complete source code structured in modular components, experimental datasets containing labeled audio samples, and supplementary resources. We elaborate on the system's design principles including signal preprocessing pipelines, the emotion classification algorithm workflow, experimental methodologies for model validation, and performance evaluation results. Furthermore, we examine the system's practical application potential in human-computer interaction scenarios and discuss possible enhancement directions such as deep learning architecture integration and real-time processing optimization. This work aims to provide valuable references for researchers and developers in the speech emotion recognition domain.