MrCuber / Speech-Emotion-Recognition

Welcome to the world of Speech Emotion Recognition (SER) in Python! This project aims to harness the power of machine learning to detect and classify emotions from spoken language. Whether it's joy, sadness, anger, or any other emotion, our SER model, built using Python libraries and deep learning techniques, can understand and differentiate them.

Speech Emotion Recognition

Overview

This GitHub repository is dedicated to the development of a Speech Emotion Recognition (SER) model using Python libraries Convolutional Neural Network, Keras, Pandas, Numpy. The goal of this project is to create an efficient and accurate model that can recognize emotions in spoken language, which can have a wide range of applications in fields such as human-computer interaction, customer service, and mental health.

Features

Speech data preprocessing: Utilizes various Python libraries to preprocess and clean speech data

Deep Learning Model: Implemented a deep learning model, Convolutional Neural Network (CNN) to extract meaningful features from the audio data.

Emotion Classification: Trained the model to classify speech into different emotion categories, such as happiness, sadness, anger, fear, etc.

Evaluation Metrics: Computes and display the evaluation metrics like accuracy, F1-score, and confusion matrix to assess the model's performance.

Usage

1. Fork this repository and Clone this repository to your local machine using
git clone https://github.com/"YOUR_GITHUB_USERNAME"/Speech-Emotion-Recognition.git
2. Set up a Python environment and install the required dependencies using pip install -r requirements.txt.
3. Prepare your speech emotion dataset and organize it appropriately.
4. Preprocess the data, train the SER model, and evaluate its performance using the provided scripts.
5. Fine-tune the model and experiment with different hyperparameters for improved accuracy.

Contributions

Contributions to this project are welcome! Whether you want to improve the model's architecture, add new features, or fix bugs, please feel free to submit pull requests.

Acknowledgments

We would like to acknowledge the open-source community and the developers of the Python libraries and frameworks used in this project. Additionally, special thanks to anyone who contributes to this project to make speech emotion recognition more accessible and accurate.

Get ready to explore the fascinating world of speech emotion recognition with Python. Happy coding!