🎙️ Human Voice Emotion Prediction System
🔍 Overview
This project is a deep learning-based human voice emotion prediction system that analyzes human speech samples to predict emotions. The system converts audio inputs into Mel spectrograms, extracts key features using a Convolutional Neural Network (CNN), and compares them with a dataset to classify emotions accurately.
🚀 Features
🎤 Speech-to-Emotion Analysis: Predicts emotions from voice samples. 🖼️ Mel Spectrogram Conversion: Converts audio files into spectrogram images for feature extraction. 🧠 CNN-Based Model: Uses Convolutional Neural Networks to extract features and classify emotions. 📊 Multi-Class Emotion Detection: Supports multiple emotions such as Happy, Sad, Angry, Neutral, etc. 📂 Pretrained & Custom Dataset Support: Works with publicly available emotional speech datasets. 🔍 Data Preprocessing: Includes noise removal, resampling, and normalization. 📈 Performance Evaluation: Model tested using accuracy, confusion matrix, and loss/accuracy plots.
🏗️ Tech Stack & Tools
Python 🐍 TensorFlow/Keras 🧠 Librosa 🎵 (for audio processing) Matplotlib & Seaborn 📊 (for visualization) Scikit-learn 🛠️ (for data preprocessing & evaluation) NumPy & Pandas 📑 (for data handling) Jupyter Notebook/Google Colab 💻 (for model training & testing)
📁 Dataset
Used the RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) dataset. Supports other emotion recognition datasets such as TESS, EMO-DB, CREMA-D.
📊 Model Performance
Training Accuracy: ~85-90% Validation Accuracy: ~80-85% Confusion Matrix Analysis: Shows strong classification between different emotions.
📌 Future Improvements
✅ Improve model generalization with more diverse datasets. ✅ Experiment with LSTM + CNN hybrid models for better sequential feature extraction. ✅ Deploy as a web application using Flask/Django. ✅ Integrate real-time emotion recognition via microphone input.
🤝 Let's Connect!
💼 Portfolio: https://www.linkedin.com/in/deepak-tetame-198932211 📧 Email: tetamedeepak@gmail.com 🐙 GitHub: github.com/Deepak-Tetame