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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

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Published on: September 27, 2024

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A Pre-Training Framework Based on Multi-Order Acoustic Simulation for Replay Voice Spoofing Detection.

Changhwan Go1, Nam In Park2, Oc-Yeub Jeon2

  • 1Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea.

Sensors (Basel, Switzerland)
|August 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel pre-training framework using multi-order acoustic simulation to improve replay voice spoofing detection in automatic speaker verification systems. The proposed method significantly enhances accuracy and F1-score compared to conventional approaches.

Keywords:
acoustic configurationdeep learningvoice spoofing

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Area of Science:

  • Speech processing
  • Machine learning
  • Cybersecurity

Background:

  • Automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, including replay attacks.
  • Existing deep learning countermeasures face challenges in dataset creation for replay attacks due to physical recording requirements.
  • Replay attacks involve mimicking a user's voice through various means, posing a significant security risk.

Purpose of the Study:

  • To propose a pre-training framework using multi-order acoustic simulation for effective replay voice spoofing detection.
  • To overcome the limitations of dataset construction for replay spoofing countermeasures.
  • To enhance the performance of deep learning models in identifying replay-based voice spoofing.

Main Methods:

  • Developed a multi-order acoustic simulation framework utilizing clean speech and room impulse response (RIR) datasets.
  • Generated simulated audio data representing clean signals, original speaker configurations, and replay attack configurations.
  • Employed a pre-training strategy to classify simulated audio, followed by fine-tuning on an existing replay voice spoofing dataset.

Main Results:

  • The proposed pre-training method achieved a high accuracy of 98.16% and an F1-score of 95.08%.
  • This represents a significant improvement over the conventional method, which obtained 92.94% accuracy and 86.92% F1-score.
  • The multi-order acoustic simulation effectively captured various acoustic configurations relevant to replay attacks.

Conclusions:

  • The proposed multi-order acoustic simulation pre-training framework is highly effective for replay voice spoofing detection.
  • This approach offers a viable solution to the data scarcity problem in training replay attack countermeasures.
  • The enhanced performance demonstrates the potential of simulated acoustic environments in bolstering ASV system security.