🎙️ Voice-Based Emotion Detection using MLP Classifier and SMS Alerts

This GitHub repository hosts a powerful and user-friendly emotion detection system that utilizes a Multilayer Perceptron (MLP) classifier to analyze voice recordings in real-time. The system is designed to accurately identify various emotions such as happiness, sadness, anger, and more, from audio input.

🚀 Key Features:

MLP Classifier: Our emotion detection model is built upon a state-of-the-art Multilayer Perceptron neural network. The model is trained on a diverse dataset, ensuring robust performance across a wide range of voice inputs.

Real-time Analysis: The system offers real-time emotion analysis, making it suitable for applications like call centers, virtual assistants, and mental health support services.

SMS Alerts: In addition to the emotion classification, the system is equipped with an SMS alert functionality. When certain emotions are detected, the system can automatically send customizable SMS alerts, allowing for timely intervention or follow-up actions. Sinchapi where it is used to get the messages by providing our phone number and by placing our small code in it to get detected output to our mobile as message.

User-Friendly Interface: We've developed an intuitive user interface that enables easy interaction with the system. Users can record voice snippets directly through the interface and receive instant emotion analysis results.

Scalable and Customizable: The architecture of the system is designed to be scalable, and the MLP model can be fine-tuned or replaced with other classifiers depending on specific use cases.

🛠️ Usage:

Clone the repository and install the required dependencies. Launch the user interface and initiate voice recording. Experience real-time emotion detection and SMS alerts in action.

📚 Resources:

Detailed documentation to help you set up and understand the project. A curated dataset for training and testing the emotion detection model. Code snippets for integrating SMS functionality into your applications.

🌐 Contributions:

We welcome contributions from the open-source community to enhance the system's capabilities, improve model accuracy, and extend its functionalities.

📞 Contact:

For inquiries, collaborations, or suggestions, please reach out to me on linked in https://www.linkedin.com/in/siri-chandana-mannava/

Start building smarter and more responsive applications with our voice-based emotion detection system. Empower your projects with the ability to understand and respond to users' emotions in real time!

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