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Improving Voice Spoofing Detection Through Extensive Analysis of Multicepstral Feature Reduction.

Leonardo Mendes de Souza1, Rodrigo Capobianco Guido1, Rodrigo Colnago Contreras2

  • 1Department of Computer Science and Statistics, Institute of Biosciences, Letters and Exact Sciences, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil.

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Summary
This summary is machine-generated.

This study enhances voice biometric security by using dimensionality reduction techniques to detect AI-generated spoofing attacks. Applying methods like PCA and Random Forest feature importance significantly improves spoof detection accuracy, achieving an Equal Error Rate (EER) of around 10%.

Keywords:
cepstral analysisdimensionality reductionmachine learningpattern recognitionspoofing detection

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Area of Science:

  • Biometrics and Security
  • Artificial Intelligence and Machine Learning
  • Signal Processing

Background:

  • Voice biometric systems are crucial for security but vulnerable to AI-powered spoofing attacks.
  • Realistic synthetic speech poses a significant threat to current voice authentication methods.
  • Developing robust defenses against sophisticated spoofing is essential for maintaining system integrity.

Purpose of the Study:

  • To explore and evaluate various dimensionality reduction strategies for identifying spoofed voice signals.
  • To enhance the effectiveness of supervised machine learning models in voice anti-spoofing.
  • To assess the performance gains achieved through dimensionality reduction in voice biometric security.

Main Methods:

  • Extraction of multi-cepstral features from voice signals.
  • Application of diverse dimensionality reduction techniques: PCA, SVD, ANOVA F-value, Mutual Information, RFE, LASSO, Random Forest importance, Permutation Importance.
  • Empirical evaluation using the ASVSpoof 2017 v2.0 dataset and Equal Error Rate (EER) metric.

Main Results:

  • Dimensionality reduction methods significantly improved the performance of spoof detection.
  • Achieved an Equal Error Rate (EER) of approximately 10% on the ASVSpoof 2017 v2.0 dataset.
  • Demonstrated the effectiveness of feature selection and reduction in combating voice spoofing.

Conclusions:

  • Dimensionality reduction is a valuable approach for enhancing voice biometric security against AI-driven spoofing.
  • The proposed framework offers improved accuracy and robustness for voice authentication systems.
  • Further research into advanced feature engineering and reduction techniques can bolster defenses against evolving threats.