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Updated: May 12, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Minimum classification error-based weighted support vector machine kernels for speaker verification.

Youngjoo Suh¹, Hoirin Kim

¹Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, South Korea. yjsuh@kaist.ac.kr

The Journal of the Acoustical Society of America

|April 6, 2013

Summary

This study introduces a novel speaker verification method using weighted kernels within Gaussian mixture model supervector space. This approach significantly enhances accuracy by minimizing speaker verification errors.

Related Experiment Videos

Last Updated: May 12, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

Machine Learning
Speech Processing
Biometrics

Background:

Support Vector Machines (SVMs) are effective for speaker verification.
Kernel function selection is crucial for SVM performance.
Gaussian Mixture Model (GMM) supervectors are used in speaker recognition.

Purpose of the Study:

To propose a new SVM-based speaker verification method.
To improve speaker verification accuracy using weighted kernels.
To address the challenge of optimal kernel selection in SVMs.

Main Methods:

Utilizing weighted kernels in the GMM supervector space.
Deriving weighted kernels through discriminative training.
Minimizing speaker verification errors during training.

Main Results:

The proposed method demonstrated substantially improved performance.
Experimental results on the NIST 2008 task confirmed effectiveness.
Outperformed the baseline kernel-based SVM method.

Conclusions:

The proposed weighted kernel SVM approach is highly effective for speaker verification.
Discriminative training of kernels enhances performance.
This method offers a significant advancement in speaker recognition technology.