This study investigates the effectiveness of different emotion detection algorithms in Speech Emotion Recognition (SER) for the accurate classification of human emotions based on audio signals. The focus is on machine learning and signal processing techniques to classify emotions. Using feature extraction methods such as Mel-Frequency Cepstrum Coefficients (MFCC), Zero-Crossing Rate (ZCR), and chroma vectors, the research evaluates several traditional and deep learning models, including CNNs, RNNs, and hybrid CNN-LSTM architectures. The results show significant improvements in accuracy when time domain and frequency based features are combined, with the best performing model achieving over 85% accuracy on widely used data sets such as CREMA-D and RAVDESS. The results underline the potential of advanced SER models to improve the interaction between humans and computers, but also highlight the need for further research into robust and unbiased models for real-world applications.