Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Audio Deepfake Detection with Self-Supervised WavLM and Multi-Fusion Attentive Classifier
summary: With the rapid development of speech synthesis and voice conversion
technologies, Audio Deepfake has become a serious threat to the Automatic
Speaker Verification (ASV) system. Numerous countermeasures are proposed to
detect this type of attack. In this paper, we report our efforts to combine the
self-supervised WavLM model and Multi-Fusion Attentive classifier for audio
deepfake detection. Our method exploits the WavLM model to extract features
that are more conducive to spoofing detection for the first time. Then, we
propose a novel Multi-Fusion Attentive (MFA) classifier based on the Attentive
Statistics Pooling (ASP) layer. The MFA captures the complementary information
of audio features at both time and layer levels. Experiments demonstrate that
our methods achieve state-of-the-art results on the ASVspoof 2021 DF set and
provide competitive results on the ASVspoof 2019 and 2021 LA set.
Thunk you very much for contribution!
Your judgement is refrected in arXivSearches.json, and is going to be used for VCLab's activity.
Thunk you so much.
Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Audio Deepfake Detection with Self-Supervised WavLM and Multi-Fusion Attentive Classifier
summary: With the rapid development of speech synthesis and voice conversion technologies, Audio Deepfake has become a serious threat to the Automatic Speaker Verification (ASV) system. Numerous countermeasures are proposed to detect this type of attack. In this paper, we report our efforts to combine the self-supervised WavLM model and Multi-Fusion Attentive classifier for audio deepfake detection. Our method exploits the WavLM model to extract features that are more conducive to spoofing detection for the first time. Then, we propose a novel Multi-Fusion Attentive (MFA) classifier based on the Attentive Statistics Pooling (ASP) layer. The MFA captures the complementary information of audio features at both time and layer levels. Experiments demonstrate that our methods achieve state-of-the-art results on the ASVspoof 2021 DF set and provide competitive results on the ASVspoof 2019 and 2021 LA set.
id: http://arxiv.org/abs/2312.08089v1
judge
Write [vclab::confirmed] or [vclab::excluded] in comment.