Voice Biometric Authentication Using AI: A Comparative Study on Neural Network Robustness to Noise and Spoofing
신경망의 노이즈 및 스푸핑에 대한 강인성 비교 를 이용한 음성 생체 인식 인증
Bayazov O. Aidos A. Kang J.W. Mukasheva A.
January 2025Korean Institute of Electrical Engineers
Transactions of the Korean Institute of Electrical Engineers
2025#74Issue 101731 - 1739 pp.
Voice biometrics is emerging as a secure, intuitive, and contactless method of identity verification, offering key advantages over traditional PIN- or password-based systems. However, its effectiveness is often reduced by real-world factors such as background noise, device variability, and spoofing attacks including replay and synthetic voice input. This paper presents a comparative analysis of three neural network architectures-Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and transformer-based Wav2Vec 2.0-for voice biometric authentication under both clean and adverse conditions. Experiments were conducted using two large-scale datasets, Mozilla Common Voice and VoxCeleb, with audio represented as mel spectrograms, mel-frequency cepstral soefficients (MFCCs), and raw waveforms. Data augmentation included Gaussian noise, reverberation, background speech, and spoofing via text-to-speech (TTS) synthesis. Results show that Wav2Vec 2.0 consistently outperforms CNN and LSTM in terms of accuracy, robustness to noise, and partial resistance to spoofing, reaching up to 92% accuracy in clean scenarios. Despite these gains, none of the models proved fully resistant to high-fidelity synthetic voice attacks. To address this, we propose integrating explicit spoof detection modules and adversarial training techniques. Additionally, privacy-preserving frameworks such as federated learning and the use of multimodal biometrics are discussed as future directions for secure and ethical deployment.
Deep learning , Federated learning , LSTM , Multimodal authentication , Spoof detection , Voice biometrics , Wav2Vec 2.0
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Dept. of Transportation System Engineering, Korea National University of Transportation, South Korea
School of Information Technology and Engineering, Kazakh-British Technical University, Kazakhstan
School of Science and Humanities, Nazarbayev University, Astana, Kazakhstan
Dept. of Transportation System Engineering
School of Information Technology and Engineering
School of Science and Humanities
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