Speech Emotion Recognition

This project focuses on enabling machines to understand human emotions from speech.

Key Features:

• Identifies seven types of emotions: anger, disgust, fear, happiness, pleasant surprise, sadness, and neutral.
• Long Short-Term Memory (LSTM) layers for handling sequential data.
• MFCCs for capturing the phonetic properties of speech.
• Validation Accuracy: 67%

Technologies Used

• Python
• Keras: building and training the neural network model
• Librosa: audio processing and feature extraction
• Pandas: data manipulation
• Matplotlib and Seaborn: data visualization

Dataset Information

• Source: Toronto Emotional Speech Set (TESS)
• Details: 2800 audio samples in WAV format recorded at 22050 Hz. Each sample is up to 3 seconds long.

Summary of the Model

• 1 LSTM layer with 256 units
• 2 Dense layers with 128 and 64 units using ReLU activation
• Dropout layers with a rate of 20% after each Dense layer
• Output layer with 7 units using softmax activation
• Optimizer: Adam
• Loss Function: Categorical Cross-Entropy
• Training Duration: 50 epochs with a batch size of 64

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