인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
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.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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UCI(KEPA) : I410-151-26-02-094486874
