The ASVspoof 2017 Challenge: Assessing the Limits of Replay Spoofing Attack Detection

By Tomi H. Kinnunen, Md. Sahidullah, Héctor Delgado, Massimiliano Todisco, N. Evans, Junichi Yamagishi, Kong Aik LEE. Interspeech, 2017

605 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/66e1dff1be7ad92104ef33a031b3392517a19b58

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Citations in the corpus

An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning (2020, background): “The main focus of the 2017 challenge was a replay task, where a large quantity of real-world replay speech data were collected [300].”
STC Antispoofing Systems for the ASVspoof2019 Challenge (2019, result): “This behaviour differs from the earlier experiments on ASVspoof2015 [11] and ASVspoof2017 [12] datasets, that did not contain artificially produced data.”
One-Class Learning Towards Synthetic Voice Spoofing Detection (2020, background): “[6], [7] has been providing datasets and metrics for anti-spoofing speaker verification research.”
ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection (2019, background): “The ASVspoof initiative [1, 2, 3] spearheads research in antispoofing for automatic speaker verification (ASV).”
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech (2019, background): “The second edition, held in 2017, focused instead on the development of countermeasures for the detection of replay spoofing attacks [6].”
ASVspoof 2019: Automatic Speaker Veriﬁcation Spooﬁng and Countermeasures Challenge Evaluation Plan ∗ (2021, background): “The ASVspoof 2019 challenge follows on from three special sessions on spoofing and countermeasures for automatic speaker verification held during INTERSPEECH 2013 [1], 2015 [2] and 2017 [3].”
ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems (2019, methodology): “7% reported in [34]) on the evaluation set of the RedDots Replayed data set, but performs much worse on the ReMASC data set (note that the lower bound of EER is 50%), indicating that the performance of the anti-spoofing model is sensitive to the environment and replay/recording settings, and may fai”
ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection (2021, background): “ASVspoof 2021 is the forth edition in a series of biannual challenges [3, 4, 5] in which participants are tasked with the design of CMs for one or more tasks.”