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Related Experiment Video

Updated: May 16, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

GFM-based methods for speaker identification.

Saurabh Bhardwaj¹, Smriti Srivastava, Madasu Hanmandlu

¹Netaji Subhas Institute of Technology, University of Delhi, New Delhi 110 078, India.

IEEE Transactions on Cybernetics

|November 30, 2012

Summary

This summary is machine-generated.

This study introduces three novel speaker identification methods combining hidden Markov models (HMM) and generalized fuzzy models (GFM), outperforming existing models. These advanced techniques demonstrate robust performance, even with noisy speech data.

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Area of Science:

Speech Processing
Artificial Intelligence
Pattern Recognition

Background:

Speaker identification is crucial for security and personalized systems.
Existing models face challenges with noisy environments and feature extraction.
Hybrid models offer potential for improved accuracy and robustness.

Purpose of the Study:

To propose three novel speaker identification methods.
To leverage the strengths of hidden Markov models (HMM) and generalized fuzzy models (GFM).
To evaluate model performance in real-world and noisy conditions.

Main Methods:

Method 1: HMM for feature extraction, GFM for speaker identification.
Method 2: Gaussian mixture model (GMM) and GFM for speaker identification.
Method 3: A novel approach inspired by human social network analysis for speaker identification.

Main Results:

All proposed models (HMM-GFM, GMM-GFM, HMM-GFM fusion) showed superior performance.
Models were validated on VoxForge and NIST 2003 datasets.
Performance remained strong even with corrupted speech data across various noise levels.

Conclusions:

The proposed hybrid HMM-GFM and GMM-GFM models offer significant improvements in speaker identification.
The fusion and social-network-inspired methods show promise for enhanced accuracy.
These models provide a robust solution for speaker identification in challenging acoustic environments.