Human Voice Recognition System

Feed Forward Neural Networks for Speaker Recognition

Overview

This project implements an advanced human voice recognition system using feed forward neural networks for speaker identification and verification platforms. The system leverages deep learning and digital signal processing techniques to achieve high accuracy in voice recognition tasks.

Project Objectives

  • Develop robust speaker recognition system
  • Achieve high accuracy in voice identification
  • Minimize Equal Error Rate (EER)
  • Create platform-independent solution

Technical Approach

Digital Signal Processing

Feature Extraction Methods:

  1. MFCC (Mel-Frequency Cepstral Coefficients)
    • Standard feature for speech recognition
    • Captures spectral envelope information
    • Mimics human auditory perception
  2. FBANK (Filter Bank Features)
    • Complementary to MFCC
    • Preserves more spectral information
    • Useful for neural network input

Deep Learning Architecture

Feed Forward Neural Networks:

  • Multi-layer perceptron architecture
  • Optimized for voice pattern recognition
  • Trained on extracted audio features
System architecture showing signal processing and neural network components.

Implementation Details

Technology Stack

  • Programming Language: Python 3
  • Deep Learning Frameworks: PyTorch, TensorFlow, Scikit-learn
  • Signal Processing: NumPy, Librosa
  • Audio Analysis: Digital signal processing libraries

System Pipeline

  1. Audio Input: Capture or load voice samples
  2. Preprocessing: Noise reduction, normalization
  3. Feature Extraction: MFCC/FBANK computation
  4. Neural Network: Feed forward classification
  5. Output: Speaker identification/verification

Performance Metrics

Evaluation Criteria

  • Accuracy: Overall classification correctness
  • F1-Score: Balanced precision and recall
  • EER (Equal Error Rate): System reliability metric
    • Lower EER indicates better performance
    • Critical for security applications

Results

  • High accuracy compared to traditional methods
  • Low Equal Error Rate (EER)
  • Robust performance across different speakers
  • Significant improvements over baseline systems

Applications

Security & Authentication

  • Biometric access control
  • Voice-based authentication
  • Secure banking systems

User Experience

  • Voice assistants personalization
  • Smart home automation
  • Adaptive user interfaces

Enterprise Solutions

  • Call center verification
  • Forensic analysis
  • Customer service automation

Research Contribution

Master’s Thesis
Institution: National School of Engineering (ENI-ABT), Bamako, Mali
Supervisor: Dr. Abdoulaye Sidibe
Period: 2019-2020

Technical Innovations

  1. Hybrid Feature Approach: Combined MFCC and FBANK features
  2. Optimized Network Architecture: Tuned for voice recognition
  3. Efficient Processing: Real-time capable implementation
  4. Cross-Platform: Platform-independent design

Challenges & Solutions

Challenge 1: Background Noise

Solution: Advanced preprocessing and noise reduction algorithms

Challenge 2: Speaker Variability

Solution: Extensive training on diverse voice samples

Challenge 3: Computational Efficiency

Solution: Optimized neural network architecture

Challenge 4: Feature Selection

Solution: Combined MFCC and FBANK for comprehensive representation

Code & Implementation

  • Modular Python codebase
  • Well-documented functions
  • Reusable components
  • Extensible architecture

Future Enhancements

  • Deep neural networks (RNNs, CNNs)
  • Larger training datasets
  • Multi-language support
  • Real-time mobile implementation
  • Cloud-based recognition service
  • Continuous learning capability

Technologies Demonstrated

  • Deep Learning (PyTorch, TensorFlow)
  • Signal Processing (NumPy, Librosa)
  • Audio Analysis
  • Pattern Recognition
  • Machine Learning (Scikit-learn)
  • Python Development

References