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pro vyhledávání: '"Evans, Nicholas"'
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
When decisions are made and when personal data is treated by automated processes, there is an expectation of fairness -- that members of different demographic groups receive equitable treatment. This expectation applies to biometric systems such as a
Externí odkaz:
http://arxiv.org/abs/2404.17810
Autor:
Tomashenko, Natalia, Miao, Xiaoxiao, Champion, Pierre, Meyer, Sarina, Wang, Xin, Vincent, Emmanuel, Panariello, Michele, Evans, Nicholas, Yamagishi, Junichi, Todisco, Massimiliano
The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional states. The organizers provide development and evaluation datasets and
Externí odkaz:
http://arxiv.org/abs/2404.02677
Autor:
Shim, Hye-jin, Jung, Jee-weon, Kinnunen, Tomi, Evans, Nicholas, Bonastre, Jean-Francois, Lapidot, Itshak
Spoofing detection is today a mainstream research topic. Standard metrics can be applied to evaluate the performance of isolated spoofing detection solutions and others have been proposed to support their evaluation when they are combined with speake
Externí odkaz:
http://arxiv.org/abs/2403.01355
The vast majority of approaches to speaker anonymization involve the extraction of fundamental frequency estimates, linguistic features and a speaker embedding which is perturbed to obfuscate the speaker identity before an anonymized speech waveform
Externí odkaz:
http://arxiv.org/abs/2309.14129
Presentation attack (spoofing) detection (PAD) typically operates alongside biometric verification to improve reliablity in the face of spoofing attacks. Even though the two sub-systems operate in tandem to solve the single task of reliable biometric
Externí odkaz:
http://arxiv.org/abs/2309.12237