Voice Emotion - Decoding Feelings from Speech
This project focuses on detecting human emotions from speech using deep learning techniques. It leverages audio data from sources like RAVDESS, CREMA-D, and TESS datasets to train models that can classify emotions based on features extracted from speech. Technologies used include Python with libraries like Librosa for audio processing, Keras and TensorFlow for building and training neural network models, and Matplotlib and Seaborn for data visualization.
Clone the repository: git clone https://github.com/yourusername/emotion-detection-from-speech.git
Install required libraries: pip install pandas numpy matplotlib seaborn librosa keras tensorflow
Make sure you have Python installed on your machine. This project is compatible with Python 3.x.Datasets The project uses the following datasets:
RAVDESS: The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset. CREMA-D: Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D). TESS: Toronto emotional speech set (TESS).
kaggle datasets download -d uwrfkaggler/ravdess-emotional-speech-audio
kaggle datasets download -d ejlok1/cremad
kaggle datasets download -d ejlok1/toronto-emotional-speech-set-tess
Navigate to the project directory. Execute the main script: python emotion_detection_script.py
Features Audio signal processing and feature extraction. Training deep learning models to classify emotions. Visualization of training results and accuracy metrics.
Contributions are welcome! Please read the CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
This project is licensed under the MIT License - see the LICENSE.md file for details.
Acknowledgments Thanks to Kaggle for providing the datasets used in this project. Inspired by research in audio signal processing and emotional intelligence in speech.