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Robust text-dependent speaker verification system using gender aware Siamese-Triplet Deep Neural Network.

Sanghamitra V Arora1

  • 1Department of Electronics and Communication Engineering, Dronacharya Group of Institutions, Greater Noida, UP, India.

Network (Bristol, England)
|December 29, 2024
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Summary
This summary is machine-generated.

This study introduces a Gender-Aware Siamese-Triplet Network-Deep Neural Network (ST-DNN) for improved speaker verification. The novel architecture significantly reduces error rates, enhancing security in voice authentication systems.

Keywords:
Siamese networkSpeaker verificationgender informationstage-wise trainingtriplet network

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Area of Science:

  • Speech processing
  • Machine learning
  • Biometrics

Background:

  • Text-dependent speaker verification is crucial for security but challenged by voice variations.
  • Existing methods struggle with linguistic diversity and gender-specific pitch differences, impacting accuracy.

Purpose of the Study:

  • To introduce a Gender-Aware Siamese-Triplet Network-Deep Neural Network (ST-DNN) for enhanced speaker verification.
  • To address challenges in speaker authentication caused by voice quality, linguistic diversity, and gender differences.

Main Methods:

  • Utilized Convolutional 2D layers with ReLU activation for feature extraction.
  • Implemented multi-fusion dense skip connections and batch normalization for feature integration.
  • Employed separate male and female ST-DNN models, incorporating Individual, Siamese, and Triplet Networks.

Main Results:

  • Achieved significant reductions in Equal Error Rate (EER) for males (32.31%–54.55%) and females (33.73%–38.98%).
  • Demonstrated substantial reductions in Minimum Decision Cost Function (MinDCF) for males (53.47%–86.36%) and females (39.46%–71.19%).
  • Validated the ST-DNN architecture's efficacy on RSR2015 and RedDots Challenge 2016 datasets.

Conclusions:

  • The Gender-Aware ST-DNN architecture effectively improves text-dependent speaker verification accuracy.
  • The proposed method robustly handles variations in voice quality, linguistic diversity, and gender-specific characteristics.
  • Results confirm the ST-DNN's suitability for real-world high-security voice authentication applications.