Project Title : Speech Emotion Recognition

Objective:
To develop a machine learning model that can automatically detect and classify emotions (such as happy, sad, angry, neutral) from speech recordings.

What It Does:
The system analyzes audio features extracted from speech signals and uses machine learning models to identify the underlying emotional state of the speaker.

Key Concepts:

Speech Signal Processing: Analyzing audio signals to extract meaningful features.

Feature Extraction: Extracting characteristics such as pitch, tone, tempo, and intensity.

Emotion Classification: Using machine learning algorithms to classify emotions based on these extracted features.

Steps Involved:

Dataset Collection:

Use public datasets like RAVDESS, TESS, or EMO-DB that contain audio recordings of different emotions.

Each audio sample is labeled with the corresponding emotion (e.g., happy, sad, angry, surprised).

Preprocessing:

Convert audio files into a suitable format (e.g., WAV, MP3) and extract the speech segments.

Normalize the audio signals (e.g., adjusting volume levels) to reduce noise.

Apply noise reduction techniques to clean the data.

Feature Extraction:

Extract features such as MFCCs (Mel Frequency Cepstral Coefficients), Chroma features, Spectral Contrast, or Zero-Crossing Rate to represent the audio signals.

Features like pitch, energy, and formants can help in identifying emotional tone.

Model Building:

Machine Learning Models: Use models like SVM (Support Vector Machine), Random Forest, or KNN for classifying emotions based on features.

Deep Learning Models: Use CNNs (Convolutional Neural Networks) or RNNs (Recurrent Neural Networks) for learning from raw audio signals, especially for larger, more complex datasets.

Transfer Learning: Use pre-trained models (e.g., models trained on large speech datasets) for better performance, especially with limited data.

Model Evaluation:

Evaluate the model's performance using metrics like accuracy, precision, recall, and F1-score.

Use a confusion matrix to analyze which emotions are most commonly misclassified.

Deployment (Optional):

Develop a real-time system that processes live audio input (using a microphone) and predicts the emotion of the speaker.

Integrate the model into applications like virtual assistants, customer support systems, or health monitoring systems for emotion detection.

Applications:

Customer Support: Emotion recognition for improving customer interactions in call centers.

Mental Health: Detecting emotional states to help monitor mental health conditions.

Human-Computer Interaction: Adding emotion-aware responses to virtual assistants like Siri or Alexa.

Education: Emotion-aware tutoring systems that adapt based on students' emotional responses.

Media & Entertainment: Analyzing emotions in movies, games, or advertisements for better audience engagement.

Tools & Technologies:

Languages: Python

Libraries: Librosa (for audio processing), Scikit-learn, TensorFlow/Keras, PyTorch

Platforms: Jupyter Notebooks for development, Google Colab for training models