Speech Emotion Recognition

This project focuses on recognizing emotions from speech using machine learning techniques. The system analyzes audio recordings to classify emotions such as happy, sad, angry, or neutral based on the tonal characteristics and features of the speech.

Features

Detects and classifies emotions from spoken audio.
Uses audio features such as pitch, tone, and speech rate for emotion recognition.
Employs machine learning models like Random Forest, SVM, or neural networks for classification.
Provides a user-friendly interface to upload audio files and receive emotion predictions.

Installation

To set up the Speech Emotion Recognition system on your local machine, follow these steps:

Clone the Repository

git clone https://github.com/username/speech-emotion-recognition.git
cd speech-emotion-recognition

Set Up Virtual Environment (Optional but Recommended)

python3 -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

Install Required Packages
```
pip install -r requirements.txt
```

Usage

To start recognizing emotions from speech, run the main script and upload your audio recordings through the web interface:

python app.py

Access the application through your web browser, upload an audio file, and get emotion recognition results.

Results

The system provides accurate emotion recognition based on the analyzed speech characteristics, classifying emotions into categories such as happy, sad, angry, or neutral.