Project Title : Emotion Detection from Voice

Objective:
To build a system that can recognize and classify human emotions (like happy, sad, angry, neutral, etc.) by analyzing the speaker’s voice using artificial intelligence techniques.

Key Concepts:

Speech Emotion Recognition (SER) uses voice signals to identify emotions.

It applies machine learning (ML) or deep learning (DL) models trained on voice datasets.

Uses audio features like pitch, tone, frequency, MFCC (Mel-Frequency Cepstral Coefficients), energy, etc.

Steps Involved:

Dataset Collection:
Use publicly available datasets like RAVDESS, SAVEE, or TESS which include labeled emotional audio samples.

Preprocessing:

Clean and normalize audio data.

Extract relevant features like MFCC, chroma, spectral contrast.

Model Building:

Use ML algorithms (e.g., SVM, Random Forest) or DL models (e.g., CNN, LSTM).

Train the model on feature vectors labeled with emotions.

Model Evaluation:

Measure performance using accuracy, precision, recall, and confusion matrix.

Use cross-validation to validate results.

Deployment (Optional):

Create a simple interface or integrate it into apps for real-time emotion detection.

Applications:

Virtual assistants (e.g., Alexa, Siri)

Customer service sentiment analysis

Mental health monitoring

Smart cars for driver mood detection

Tools & Technologies:

Languages: Python

Libraries: Librosa, NumPy, Scikit-learn, TensorFlow/Keras, Matplotlib