EmotionSense AI

Deep Learning-Based Speech Emotion Recognition System

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Overview

EmotionSense AI is a deep learning-powered Speech Emotion Recognition (SER) system capable of identifying human emotions directly from speech signals.

The system combines advanced audio preprocessing techniques with a hybrid CNN-BiGRU neural network architecture to learn emotional patterns from speech recordings.

The project analyzes vocal characteristics and predicts emotional states including:

• Angry
• Happy
• Sad
• Fear
• Neutral
• Disgust
• Surprise

This project demonstrates practical applications of Deep Learning, Audio Signal Processing, Speech AI, and Human-Centered AI systems.

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Business Problem

Human emotions play a critical role in communication.

Traditional systems understand what people say but often fail to understand how they feel.

Speech Emotion Recognition enables:

• Intelligent virtual assistants
• Customer sentiment analysis
• Mental health monitoring
• Human-computer interaction
• Call center analytics
• Conversational AI systems

EmotionSense AI aims to bridge this gap by automatically recognizing emotional states from speech.

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Key Features

Audio Signal Processing

• Audio preprocessing pipeline
• Feature extraction
• Noise handling
• Audio normalization

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Deep Learning Architecture

CNN Layers

• Learn local emotional speech patterns
• Extract discriminative audio features

Bidirectional GRU Layers

• Capture temporal dependencies
• Learn emotional context from speech sequences

Hybrid CNN-BiGRU Model

• Combines spatial and sequential learning
• Improves emotion classification performance

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Emotion Classification

Predicts multiple emotional states from speech recordings.

Supported emotions include:

• Angry
• Happy
• Sad
• Fear
• Neutral
• Disgust
• Surprise

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Prediction Pipeline

Audio File ↓ Preprocessing ↓ Feature Extraction ↓ CNN-BiGRU Model ↓ Emotion Prediction

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Dataset

The project utilizes:

TESS Dataset

Toronto Emotional Speech Set

Contains professionally recorded emotional speech samples.

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SAVEE Dataset

Surrey Audio-Visual Expressed Emotion Dataset

Contains emotional speech recordings from multiple speakers.

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Combined Dataset Training

The model was trained using:

• TESS Dataset
• SAVEE Dataset

to improve generalization and robustness.

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Deep Learning Pipeline

Data Collection

• TESS Dataset
• SAVEE Dataset

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Data Analysis

• Exploratory Data Analysis
• Class Distribution Analysis
• Audio Characteristics Analysis

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Data Preprocessing

• Audio Loading
• Signal Processing
• Feature Extraction
• Data Scaling
• Label Encoding

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Model Training

Hybrid Architecture:

Input Features ↓ CNN Layers ↓ BiGRU Layers ↓ Dense Layers ↓ Softmax Output

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Model Evaluation

Evaluated using:

• Accuracy
• Loss Curves
• Confusion Matrix
• Classification Metrics

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System Architecture

Audio Input ↓ Preprocessing ↓ Feature Extraction ↓ Feature Scaling ↓ CNN Layers ↓ BiGRU Layers ↓ Dense Layers ↓ Emotion Prediction

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Technologies Used

Category	Technology
Programming Language	Python
Deep Learning	TensorFlow
Neural Networks	Keras
Data Processing	NumPy
Data Analysis	Pandas
Visualization	Matplotlib
Audio Processing	Librosa
Machine Learning	Scikit-Learn
Notebook Environment	Jupyter

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Project Structure

EmotionSense-AI/
│
├── src/
│   ├── Preprocessing.ipynb
│   ├── CNN + BIGRU.ipynb
│   ├── Prediction.ipynb
│   ├── SAVEE Data EDA.ipynb
│   ├── SAVEE Data Analysis.ipynb
│   ├── TESS Data EDA.ipynb
│   ├── TESS Data Analysis.ipynb
│   ├── TESS + SAVEE Data EDA.ipynb
│   └── TESS + SAVEE Data Analysis.ipynb
│
├── models/
│   ├── audio_emotion_model.h5
│   ├── label_encoder.pkl
│   └── scaler.pkl
│
├── requirements.txt
├── LICENSE
├── README.md
└── .gitignore

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Core Capabilities

Speech Emotion Recognition

Audio Signal Processing

Deep Learning Classification

CNN Feature Learning

BiGRU Sequence Modeling

Audio Feature Extraction

Multi-Class Emotion Classification

Emotional Speech Analysis

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Applications

Virtual Assistants

Understand user emotional state.

Customer Service Analytics

Detect customer frustration or satisfaction.

Mental Health Monitoring

Analyze emotional trends in speech.

Human-Computer Interaction

Build emotion-aware AI systems.

Call Center Intelligence

Improve customer experience insights.

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Future Improvements

Streamlit Dashboard

• Audio Upload Interface
• Emotion Visualization
• Real-Time Predictions

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Real-Time Emotion Recognition

• Microphone Input
• Live Emotion Detection

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Advanced Deep Learning Models

• CNN-LSTM
• Transformers
• Wav2Vec2
• HuBERT

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Deployment

• FastAPI
• Docker
• Cloud Deployment

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Why This Project Stands Out

EmotionSense AI combines:

• Deep Learning
• Speech AI
• Audio Signal Processing
• Human Emotion Analysis

into a practical Speech Emotion Recognition system.

The project demonstrates hands-on experience in:

• Deep Learning Engineering
• Audio Processing
• Speech AI
• Neural Network Design
• End-to-End ML Pipelines

making it a strong portfolio project for AI Engineer, Machine Learning Engineer, Deep Learning Engineer, and Applied AI roles.