Cited by 63Biometric Identification and SecurityFace recognition and analysisSpeech Recognition and Synthesis

Audio-Visual Deep Neural Network for Robust Person Verification

Qian, Yanmin, Chen, Zhengyang, Wang, Shuai·2021·IEEE/ACM Transactions on Audio, Speech, and Language Processing

Abstract

Voice and face are two most popular biometrics for person verification, usually used in speaker verification and face verification tasks. It has already been observed that simply combining the information from these two modalities can lead to a more powerful and robust person verification system. In this article, to fully explore the multi-modal learning strategies for person verification, we proposed three types of audio-visual deep neural network (AVN), including feature level AVN (AVN-F), embedding level AVN (AVN-E), and embedding level combination with joint learning AVN (AVN-J). To further enhance the system robustness in real noisy conditions where not both modalities can be accessed with high-quality, we proposed several data augmentation strategies for each proposed AVN: A feature-level multi-modal data augmentation is proposed for AVN-F and an embedding-level data augmentation with novel noise distribution matching is designed for AVN-E. For AVN-J, both the feature and embedding level multi-modal data augmentation methods can be applied. All the proposed models are trained on the VoxCeleb2 dev dataset and evaluated on the standard VoxCeleb1 dataset, and the best system achieves 0.558, 0.441% and 0.793% EER on the three official trial lists of VoxCeleb1, which is to our knowledge the best published single system results on this corpus for person verification. To validate the robustness of the proposed approaches, a noisy evaluation set based on the VoxCeleb1 is constr…

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Crossref · 10.1109/taslp.2021.3057230

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  • Existing unimodal deepfake detectors are primarily designed to detect a single type of manipulation, such as visual, acoustic, and text.

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Authors
Qian, Y., Chen, Z., & Wang, S.
Affiliations
Shanghai Jiao Tong University (CN)
Venue
IEEE/ACM Transactions on Audio, Speech, and Language Processing
Published
2021
Volume
29
Pages
1079-1092
ISSN
  • 2329-9290 (ISSN-L)
  • 2329-9304
OpenAlex
W3126757411
Cited by
63
References
62
Citations by year
2026: 9 · 2025: 8 · 2024: 13 · 2023: 13 · 2022: 13 · 2021: 7
FWCI
5.36
Type
journal article
Language
en
Open access
closed
Locations
IEEE/ACM Transactions on Audio Speech and Language Processing (publishedVersion)
Funders
National Natural Science Foundation of China (62071288, U1736202)
Topics
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Keywords
human verification, multimodal fusion, speaker verification, face verification, noise robustness, data augmentation, VoxCeleb1, VoxCeleb2

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