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Spoofing Detection in Automatic Speaker Verification Systems Using DNN Classifiers and Dynamic Acoustic Features.

Hong Yu, Zheng-Hua Tan, Zhanyu Ma

    IEEE Transactions on Neural Networks and Learning Systems
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    Summary
    This summary is machine-generated.

    This study introduces a novel deep neural network (DNN) spoofing detection method using human log-likelihoods (HLLs) to enhance automatic speaker verification (ASV) security. The proposed DNN-HLL approach significantly improves spoofing detection accuracy, outperforming previous methods.

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    Area of Science:

    • Speech Processing
    • Machine Learning
    • Cybersecurity

    Background:

    • Automatic speaker verification (ASV) systems face significant security threats from sophisticated spoofing attacks enabled by advancements in speech synthesis technology.
    • Existing countermeasures often rely on distinguishing spoofed speech from genuine speech using acoustic features and classifiers like Gaussian mixture models (GMMs) and deep neural networks (DNNs).
    • Current methods typically use log-likelihood ratios (LLRs) for spoofing detection scores, which can be less effective with highly realistic spoofing attempts.

    Purpose of the Study:

    • To develop and evaluate a novel, highly effective anti-spoofing system for automatic speaker verification (ASV).
    • To propose a new scoring method based on human log-likelihoods (HLLs) that is mathematically proven to be more robust than traditional log-likelihood ratios (LLRs).
    • To investigate the efficacy of various dynamic acoustic features, including Constant Q Cepstral Coefficients (CQCC), in conjunction with a DNN-HLL classifier.

    Main Methods:

    • Training a five-layer deep neural network (DNN) classifier utilizing dynamic acoustic features for spoofing detection.
    • Implementing a novel scoring method based solely on human log-likelihoods (HLLs), mathematically demonstrated to be superior to the classical LLR scoring.
    • Evaluating the performance of five dynamic filter bank-based cepstral features and CQCC with the proposed DNN-HLL method.

    Main Results:

    • The proposed DNN-HLL method significantly enhances spoofing detection accuracy compared to the conventional GMM-LLR approach.
    • The DNN-HLL model, particularly with CQCC features, achieved an average equal error rate of 0.045% on the ASVspoof 2015 Challenge task, surpassing prior research.
    • Integrating the CQCC-based DNN-HLL system with ASV systems substantially reduces the false acceptance rate against spoofing attacks.

    Conclusions:

    • The novel DNN-HLL spoofing detection system offers a significant advancement in securing automatic speaker verification (ASV) systems against sophisticated spoofing attacks.
    • The HLL scoring method provides a more robust and accurate approach to spoofing detection, especially when spoofed speech closely mimics genuine human speech.
    • The proposed method demonstrates state-of-the-art performance and offers practical benefits for real-world ASV security applications.