Zobrazeno 1 - 10
of 1 704
pro vyhledávání: '"EVANS, Nicholas A."'
Autor:
Jung, Jee-weon, Wu, Yihan, Wang, Xin, Kim, Ji-Hoon, Maiti, Soumi, Matsunaga, Yuta, Shim, Hye-jin, Tian, Jinchuan, Evans, Nicholas, Chung, Joon Son, Zhang, Wangyou, Um, Seyun, Takamichi, Shinnosuke, Watanabe, Shinji
This paper introduces SpoofCeleb, a dataset designed for Speech Deepfake Detection (SDD) and Spoofing-robust Automatic Speaker Verification (SASV), utilizing source data from real-world conditions and spoofing attacks generated by Text-To-Speech (TTS
Externí odkaz:
http://arxiv.org/abs/2409.17285
Autor:
Jung, Jee-weon, Zhang, Wangyou, Maiti, Soumi, Wu, Yihan, Wang, Xin, Kim, Ji-Hoon, Matsunaga, Yuta, Um, Seyun, Tian, Jinchuan, Shim, Hye-jin, Evans, Nicholas, Chung, Joon Son, Takamichi, Shinnosuke, Watanabe, Shinji
Text-to-speech (TTS) systems are traditionally trained using modest databases of studio-quality, prompted or read speech collected in benign acoustic environments such as anechoic rooms. The recent literature nonetheless shows efforts to train TTS sy
Externí odkaz:
http://arxiv.org/abs/2409.08711
We introduce 2D-Malafide, a novel and lightweight adversarial attack designed to deceive face deepfake detection systems. Building upon the concept of 1D convolutional perturbations explored in the speech domain, our method leverages 2D convolutional
Externí odkaz:
http://arxiv.org/abs/2408.14143
Autor:
Todisco, Massimiliano, Panariello, Michele, Wang, Xin, Delgado, Héctor, Lee, Kong Aik, Evans, Nicholas
We present Malacopula, a neural-based generalised Hammerstein model designed to introduce adversarial perturbations to spoofed speech utterances so that they better deceive automatic speaker verification (ASV) systems. Using non-linear processes to m
Externí odkaz:
http://arxiv.org/abs/2408.09300
Autor:
Wang, Xin, Delgado, Hector, Tak, Hemlata, Jung, Jee-weon, Shim, Hye-jin, Todisco, Massimiliano, Kukanov, Ivan, Liu, Xuechen, Sahidullah, Md, Kinnunen, Tomi, Evans, Nicholas, Lee, Kong Aik, Yamagishi, Junichi
ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions. Compared to previous challenges, the ASVspoof 5 database is built from crowdsourced data
Externí odkaz:
http://arxiv.org/abs/2408.08739
Voice anonymisation can be used to help protect speaker privacy when speech data is shared with untrusted others. In most practical applications, while the voice identity should be sanitised, other attributes such as the spoken content should be pres
Externí odkaz:
http://arxiv.org/abs/2408.04306
Autor:
Panariello, Michele, Tomashenko, Natalia, Wang, Xin, Miao, Xiaoxiao, Champion, Pierre, Nourtel, Hubert, Todisco, Massimiliano, Evans, Nicholas, Vincent, Emmanuel, Yamagishi, Junichi
The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology. In this paper we present a systematic overview and analysis of the second edition held in 2022. We describe the voice anonymisation task and d
Externí odkaz:
http://arxiv.org/abs/2407.11516
Autor:
Zhang, Lin, Wang, Xin, Cooper, Erica, Diez, Mireia, Landini, Federico, Evans, Nicholas, Yamagishi, Junichi
This paper defines Spoof Diarization as a novel task in the Partial Spoof (PS) scenario. It aims to determine what spoofed when, which includes not only locating spoof regions but also clustering them according to different spoofing methods. As a pio
Externí odkaz:
http://arxiv.org/abs/2406.07816
Autor:
Jung, Jee-weon, Wang, Xin, Evans, Nicholas, Watanabe, Shinji, Shim, Hye-jin, Tak, Hemlata, Arora, Sidhhant, Yamagishi, Junichi, Chung, Joon Son
The current automatic speaker verification (ASV) task involves making binary decisions on two types of trials: target and non-target. However, emerging advancements in speech generation technology pose significant threats to the reliability of ASV sy
Externí odkaz:
http://arxiv.org/abs/2406.05339
Publikováno v:
Interspeech 2024
Recent research has highlighted a key issue in speech deepfake detection: models trained on one set of deepfakes perform poorly on others. The question arises: is this due to the continuously improving quality of Text-to-Speech (TTS) models, i.e., ar
Externí odkaz:
http://arxiv.org/abs/2406.03512