@inproceedings{217fb177849c4bd7a923bdb9c3f8c9de,
title = "Replay Spoofing Detection System for Automatic Speaker Verification Using Multi-Task Learning of Noise Classes",
abstract = "In this paper, we propose a replay attack spoofing detection system for automatic speaker verification using multi-task learning of noise classes. We define the noise that is caused by the replay attack as replay noise. We explore the effectiveness of training a deep neural network simultaneously for replay attack spoofing detection and replay noise classification. The multi-task learning includes classifying the noise of playback devices, recording environments, and recording devices as well as the spoofing detection. Each of the three types of the noise classes also includes a genuine class. The experiment results on the version 1.0 of ASVspoof2017 datasets demonstrate that the performance of our proposed system is improved by 30% relatively on the evaluation set.",
keywords = "Anti-spoofing, Multi-task learning, Replay attack, Speaker verification, Spoofing detection",
author = "Shim, {Hye Jin} and Jung, {Jee Weon} and Heo, {Hee Soo} and Yoon, {Sung Hyun} and Yu, {Ha Jin}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018 ; Conference date: 30-11-2018 Through 02-12-2018",
year = "2018",
month = dec,
day = "24",
doi = "10.1109/TAAI.2018.00046",
language = "English",
series = "Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "172--176",
booktitle = "Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018",
address = "United States",
}