Zobrazeno 1 - 10
of 107
pro vyhledávání: '"Fan, Cunhang"'
Autor:
Qin, Zhanyue, Wang, Haochuan, Liu, Deyuan, Song, Ziyang, Fan, Cunhang, Lv, Zhao, Wu, Jinlin, Lei, Zhen, Tu, Zhiying, Chu, Dianhui, Yu, Xiaoyan, Sui, Dianbo
Sequential decision-making refers to algorithms that take into account the dynamics of the environment, where early decisions affect subsequent decisions. With large language models (LLMs) demonstrating powerful capabilities between tasks, we can't h
Externí odkaz:
http://arxiv.org/abs/2406.16382
Autor:
Liu, Deyuan, Qin, Zhanyue, Wang, Hairu, Yang, Zhao, Wang, Zecheng, Rong, Fangying, Liu, Qingbin, Hao, Yanchao, Chen, Xi, Fan, Cunhang, Lv, Zhao, Tu, Zhiying, Chu, Dianhui, Li, Bo, Sui, Dianbo
While large language models (LLMs) excel in many domains, their complexity and scale challenge deployment in resource-limited environments. Current compression techniques, such as parameter pruning, often fail to effectively utilize the knowledge fro
Externí odkaz:
http://arxiv.org/abs/2406.16330
In the telephony scenarios, the fake speech detection (FSD) task to combat speech spoofing attacks is challenging. Data augmentation (DA) methods are considered effective means to address the FSD task in telephony scenarios, typically divided into ti
Externí odkaz:
http://arxiv.org/abs/2406.09664
Autor:
Chen, Yujie, Yi, Jiangyan, Xue, Jun, Wang, Chenglong, Zhang, Xiaohui, Dong, Shunbo, Zeng, Siding, Tao, Jianhua, Zhao, Lv, Fan, Cunhang
Fake artefacts for discriminating between bonafide and fake audio can exist in both short- and long-range segments. Therefore, combining local and global feature information can effectively discriminate between bonafide and fake audio. This paper pro
Externí odkaz:
http://arxiv.org/abs/2406.06086
Publikováno v:
(2024) Vol. 38 No. 8: AAAI-24 Technical Tracks 8 Vol. 38 No. 8: AAAI-24 Technical Tracks 8 Vol. 38 No. 8: AAAI-24 Technical Tracks 8 Proceedings of the AAAI Conference on Artificial Intelligence, 38(8), 8380-8388
In recent years, knowledge graph completion (KGC) models based on pre-trained language model (PLM) have shown promising results. However, the large number of parameters and high computational cost of PLM models pose challenges for their application i
Externí odkaz:
http://arxiv.org/abs/2401.12997
Most research in synthetic speech detection (SSD) focuses on improving performance on standard noise-free datasets. However, in actual situations, noise interference is usually present, causing significant performance degradation in SSD systems. To i
Externí odkaz:
http://arxiv.org/abs/2310.08869
Autor:
Fan, Cunhang, Zhang, Hongyu, Huang, Wei, Xue, Jun, Tao, Jianhua, Yi, Jiangyan, Lv, Zhao, Wu, Xiaopei
Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolut
Externí odkaz:
http://arxiv.org/abs/2309.07147
Autor:
Fan, Cunhang, Xue, Jun, Tao, Jianhua, Yi, Jiangyan, Wang, Chenglong, Zheng, Chengshi, Lv, Zhao
The rhythm of bonafide speech is often difficult to replicate, which causes that the fundamental frequency (F0) of synthetic speech is significantly different from that of real speech. It is expected that the F0 feature contains the discriminative in
Externí odkaz:
http://arxiv.org/abs/2308.09944
In this paper, we propose the multi-perspective information fusion (MPIF) Res2Net with random Specmix for fake speech detection (FSD). The main purpose of this system is to improve the model's ability to learn precise forgery information for FSD task
Externí odkaz:
http://arxiv.org/abs/2306.15389
In this paper, we propose a novel self-distillation method for fake speech detection (FSD), which can significantly improve the performance of FSD without increasing the model complexity. For FSD, some fine-grained information is very important, such
Externí odkaz:
http://arxiv.org/abs/2303.01211