SPEECH EMOTIONS DETECTION SYSTEM USING PYTHON

Project Algorithm :

Mel-frequency cepstral coefficients (MFCCs), then applies Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN/LSTM)

Project Overview :

Speech is one of the most natural forms of human communication, carrying not only linguistic information but also emotional states. Recognizing emotions from speech can enhance human-computer interaction, virtual assistants, healthcare, education, and security systems. Traditional machine learning methods often struggle to capture the complex temporal and spectral features of speech. In this project, a deep learning-based approach is used to detect emotions (such as happiness, sadness, anger, fear, neutral, etc.) from audio signals. The system preprocesses speech data into spectrograms or Mel-frequency cepstral coefficients (MFCCs), then applies Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN/LSTM), or hybrid architectures to classify emotions with higher accuracy.

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