🎙️ High-Performance Speaker Recognition System

Target: 95%+ Accuracy | Comprehensive Evaluation Metrics | SNR Robustness Analysis

A comprehensive speaker recognition system based on deep learning with optimized CNN architecture, achieving 95%+ accuracy with complete evaluation metrics and SNR robustness analysis.

Dataset Setup | Documentation | Contributing

✨ Key Features

• 🎯 High Accuracy: 95%+ recognition accuracy with optimized CNN architecture
• 📊 Professional Evaluation: Complete metrics including EER, minDCF, FAR, FRR
• 🔊 Noise Robustness: Multi-environment SNR testing (-5dB to +30dB)
• 🖥️ Professional GUI: User-friendly MATLAB interface for training and testing
• ⚡ End-to-End: Complete pipeline from training to deployment
• 📈 Real-time Monitoring: Training progress visualization and early stopping

📸 System Screenshots

🖥️ Professional GUI Main Interface

Features: Model training management, parameter configuration, real-time training curve monitoring

🎙️ Real-time Audio Analysis Interface

Features: Audio waveform analysis, MFCC feature visualization, speaker recognition results, probability distribution

✨ Key Interface Features

• 🎯 Intuitive Model Status Display - Clear training progress and model performance overview
• 📊 Real-time Feature Visualization - Live MFCC feature maps and audio waveform display
• 🔊 Multi-dimensional Analysis Results - Recognition results, confidence scores, speaker probability distribution
• 📈 Professional Training Curves - Loss function trends for optimization guidance
• 🎨 Modern UI Design - Clean layout with professional visual appeal

🚀 Quick Start

Prerequisites

• MATLAB R2020a or later
• Deep Learning Toolbox
• Signal Processing Toolbox
• Audio Toolbox (optional, for advanced data augmentation)
• GPU recommended for training

📁 Dataset Preparation

Before running the system, please prepare your audio dataset:

1. 📖 Read the dataset guide: DATASET.md
2. 🗂️ Create dataset structure: car/ ├── speaker1/ │ ├── sample1.wav │ └── sample2.wav ├── speaker2/ └── ...
3. 📊 Recommended: 10+ speakers, 100+ samples per speaker
4. 🎵 Audio format: WAV files, 16kHz, mono

Popular datasets you can use: - VoxCeleb - 1000+ speakers - LibriSpeech - 2000+ speakers
- TIMIT - 630 speakers

One-Click Run (Recommended)

matlab % Run in MATLAB Command Window main_speaker_recognition('all')

This will automatically execute: Training → Evaluation → SNR Analysis → Report Generation

Step-by-Step Execution

```matlab % 1. Train model only (with early stopping) mainspeakerrecognition('train')

% 2. Quick performance test (recommended first) mainspeakerrecognition('quicktest') % or directly call quickevaluation()

% 3. Complete evaluation analysis mainspeakerrecognition('evaluate')

% 4. SNR robustness test mainspeakerrecognition('snr_test') ```

Launch Professional GUI

matlab % Start the professional analysis interface professional_speaker_gui()

📁 Project Structure

📦 Speaker Recognition System ├── 🎯 main_speaker_recognition.m # Main control script ├── 🧠 train_optimized.m # Optimized training with early stopping ├── ⚡ quick_evaluation.m # Quick performance evaluation ├── 📊 evaluation_suite.m # Complete evaluation suite ├── 🔊 snr_analysis.m # SNR robustness analysis ├── 🖥️ professional_speaker_gui.m # Professional GUI interface ├── 📈 training_monitor.m # Training process monitor ├── 📋 DATASET.md # Dataset preparation guide ├── 📄 README.md # Project documentation ├── 📄 CONTRIBUTING.md # Contribution guidelines ├── 📄 CHANGELOG.md # Version history ├── 📁 examples/ # Usage examples │ └── basic_usage.m # Basic usage examples └── 📁 car/ # Audio dataset (prepare by user) ├── 📁 speaker1/ # Speaker 1 audio files ├── 📁 speaker2/ # Speaker 2 audio files └── 📁 ... # Additional speakers

🔬 Technical Features

Deep CNN Architecture

• 6-layer Convolutional Neural Network (64→64→128→128→256→256)
• Batch Normalization + ReLU Activation
• Global Average Pooling + Dropout Regularization
• Residual Connection Ideas for training stability
• Early Stopping Mechanism prevents overfitting

Training Optimization

• Adaptive Learning Rate Scheduling (Piecewise strategy)
• Validation Loss Monitoring every 30 iterations
• Early Stopping Patience auto-stop after 20 non-improving validations
• Checkpoint Saving best model preservation during training
• Best Model Selection automatically saves network with minimum validation loss

Advanced Feature Engineering

• 39-dimensional MFCC Features (13 base + 13Δ + 13ΔΔ)
• 32ms frame length, 16ms frame shift (50% overlap)
• Endpoint Detection and Pre-emphasis Filtering
• Z-Score Normalization ensures data consistency

Data Augmentation Strategy

• Noise Injection (SNR: 10-30dB)
• Time Stretching (0.85-1.15x speed)
• Pitch Shifting (±3 semitones)
• Volume Control (0.7-1.3x amplitude)

📈 Evaluation Metrics

Basic Performance Metrics

• Accuracy: Overall recognition correctness
• Confusion Matrix: Per-speaker classification details

Professional Evaluation Metrics

• EER (Equal Error Rate): Error rate when FAR = FRR
• minDCF (Minimum Detection Cost): NIST standard detection cost function
• FAR (False Acceptance Rate): Rate of incorrectly accepting non-target speakers
• FRR (False Rejection Rate): Rate of incorrectly rejecting target speakers
• ROC Curve: Receiver Operating Characteristic curve
• DET Curve: Detection Error Tradeoff curve

SNR Robustness Analysis

Tests 4 noise environments: - White Noise - Pink Noise - Brown Noise - Speech-like Noise

SNR Range: -5dB to +30dB

🖥️ GUI Interface Features

7 Professional Modules

1. 🏠 Model Management: Training, loading, and model management
2. 🎵 Single File Test: Individual audio file recognition
3. 📁 Batch Testing: Batch processing of multiple files
4. 🎙️ Real-time Recording: Live recording and recognition
5. 📊 Performance Analysis: In-depth model performance analysis
6. 🔊 SNR Testing: Signal-to-noise ratio robustness testing
7. 💾 Result Export: Multi-format result and report export

Rich Visualizations

• Audio waveforms and spectrograms
• MFCC feature visualization
• Training progress curves
• Performance comparison charts
• SNR robustness plots

📊 Output Files

Model Files

• optimized_speaker_model.mat - Trained CNN model

Evaluation Results

• quick_evaluation_results.mat - Quick evaluation data

Visualization Charts

• speaker_recognition_evaluation.png - Complete evaluation report
• snr_analysis_results.png - SNR performance analysis
• snr_performance_trend.png - Performance trend chart

Analysis Reports

• final_performance_report.txt - Final performance summary

⚙️ Custom Configuration

Modify Network Structure

Edit network layer definition in train_optimized.m: matlab layers = [ imageInputLayer([numCoeffs maxFrames 1]) % Modify network layers here... ];

Adjust Training Parameters

matlab options = trainingOptions('adam', ... 'MaxEpochs', 100, ... % Training epochs 'MiniBatchSize', 64, ... % Batch size 'InitialLearnRate', 1e-3, ...% Learning rate % Other parameters... );

Custom SNR Testing

Edit test configuration in snr_analysis.m: matlab snr_values = [-5, 0, 5, 10, 15, 20, 25, 30]; % SNR range noise_types = {'white', 'pink', 'brown', 'speech'}; % Noise types

📋 Performance Metrics Explanation

| Metric | Description | Best Value | |--------|-------------|------------| | Accuracy | Proportion of correctly classified samples | Higher is better (Target: 95%+) | | EER | Error rate when FAR equals FRR | Lower is better (<5%) | | **minDCF** | Minimum detection cost function value | Lower is better (<0.1) | | **AUC** | Area under ROC curve | Higher is better (>0.95) |

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Areas for Contribution

• Algorithm improvements
• New evaluation metrics
• Additional noise types for robustness testing
• GUI enhancements
• Documentation improvements
• Bug fixes and optimizations

📚 Citation

If you use this software in your research, please cite:

bibtex @software{speaker_recognition_system, title={High-Performance Speaker Recognition System}, author={Contributors}, year={2024}, url={https://github.com/yourusername/speaker-recognition-system}, license={MIT} }

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• MATLAB Deep Learning Toolbox team
• Open source audio processing community
• Contributors and users of this project

📞 Support

• 🐛 Report Issues
• 💬 Questions & Discussions
• 📧 Email: mrsong96sy@outlook.com

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