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pro vyhledávání: '"Lu, Jingze"'
Spoofing speech detection is a hot and in-demand research field. However, current spoofing speech detection systems is lack of convincing evidence. In this paper, to increase the reliability of detection systems, the flaws of rhythm information inher
Externí odkaz:
http://arxiv.org/abs/2310.12014
Current synthetic speech detection (SSD) methods perform well on certain datasets but still face issues of robustness and interpretability. A possible reason is that these methods do not analyze the deficiencies of synthetic speech. In this paper, th
Externí odkaz:
http://arxiv.org/abs/2309.16954
The current speech anti-spoofing countermeasures (CMs) show excellent performance on specific datasets. However, removing the silence of test speech through Voice Activity Detection (VAD) can severely degrade performance. In this paper, the impact of
Externí odkaz:
http://arxiv.org/abs/2309.11827
The detection of spoofing speech generated by unseen algorithms remains an unresolved challenge. One reason for the lack of generalization ability is traditional detecting systems follow the binary classification paradigm, which inherently assumes th
Externí odkaz:
http://arxiv.org/abs/2309.08285
The wav2vec 2.0 and integrated spectro-temporal graph attention network (AASIST) based countermeasure achieves great performance in speech anti-spoofing. However, current spoof speech detection systems have fixed training and evaluation durations, wh
Externí odkaz:
http://arxiv.org/abs/2309.08279
Utilizing the large-scale unlabeled data from the target domain via pseudo-label clustering algorithms is an important approach for addressing domain adaptation problems in speaker verification tasks. In this paper, we propose a novel progressive sub
Externí odkaz:
http://arxiv.org/abs/2305.12703
Autor:
Zhang, Yuxiang, Lu, Jingze, Wang, Xingming, Li, Zhuo, Xiao, Runqiu, Wang, Wenchao, Li, Ming, Zhang, Pengyuan
This paper describes the deepfake audio detection system submitted to the Audio Deep Synthesis Detection (ADD) Challenge Track 3.2 and gives an analysis of score fusion. The proposed system is a score-level fusion of several light convolutional neura
Externí odkaz:
http://arxiv.org/abs/2210.06818
Publikováno v:
In Solar Energy August 2024 278
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