Zobrazeno 1 - 10
of 723
pro vyhledávání: '"Bonastre P"'
Autor:
Salam Al-hchimy Zainab, Talib Hasson Saad, Mahmood Kareem Rajaa, Jabor Maytham S., Ramadhan Ali J., TaeiZadeh Ali, Carlos Campelo José, Bonastre Pina Alberto
Publikováno v:
BIO Web of Conferences, Vol 97, p 00048 (2024)
Wireless Sensor Network (WSN) is one of the trend technologies. It was aggregation, processing, and transferring a huge amount of data in different applications that deal with the surrounding environment. Using a huge number of sensors deployed or or
Externí odkaz:
https://doaj.org/article/d834b3b7023749b38482a5bd1eca9e45
Originating in game theory, Shapley values are widely used for explaining a machine learning model's prediction by quantifying the contribution of each feature's value to the prediction. This requires a scalar prediction as in binary classification,
Externí odkaz:
http://arxiv.org/abs/2408.01382
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
Publikováno v:
Eurospeech 1995, Sep 1995, Madrid, Spain. pp.337-340
Second-order statistical methods show very good results for automatic speaker identification in controlled recording conditions. These approaches are generally used on the entire speech material available. In this paper, we study the influence of the
Externí odkaz:
http://arxiv.org/abs/2402.16429
In the context of spoofing attacks in speaker recognition systems, we observed that the waveform probability mass function (PMF) of genuine speech differs significantly from the PMF of speech resulting from the attacks. This is true for synthesized o
Externí odkaz:
http://arxiv.org/abs/2310.05534
Autor:
Miao, Xiaoxiao, Wang, Xin, Cooper, Erica, Yamagishi, Junichi, Evans, Nicholas, Todisco, Massimiliano, Bonastre, Jean-François, Rouvier, Mickael
The success of deep learning in speaker recognition relies heavily on the use of large datasets. However, the data-hungry nature of deep learning methods has already being questioned on account the ethical, privacy, and legal concerns that arise when
Externí odkaz:
http://arxiv.org/abs/2309.06141
The consumption of online videos on the Internet grows every year, making it a market that increasingly generates a greater volume of income. This paper deals with a problem of great interest in this context: the allocation of the generated revenues
Externí odkaz:
http://arxiv.org/abs/2304.12268
Autor:
Nguyen, Tuan, Mdhaffar, Salima, Tomashenko, Natalia, Bonastre, Jean-François, Estève, Yannick
This paper presents a study on the use of federated learning to train an ASR model based on a wav2vec 2.0 model pre-trained by self supervision. Carried out on the well-known TED-LIUM 3 dataset, our experiments show that such a model can obtain, with
Externí odkaz:
http://arxiv.org/abs/2302.10790
Autor:
Noé, Paul-Gauthier, Miao, Xiaoxiao, Wang, Xin, Yamagishi, Junichi, Bonastre, Jean-François, Matrouf, Driss
The use of modern vocoders in an analysis/synthesis pipeline allows us to investigate high-quality voice conversion that can be used for privacy purposes. Here, we propose to transform the speaker embedding and the pitch in order to hide the sex of t
Externí odkaz:
http://arxiv.org/abs/2211.16065
Autor:
Tomashenko, Natalia, Srivastava, Brij Mohan Lal, Wang, Xin, Vincent, Emmanuel, Nautsch, Andreas, Yamagishi, Junichi, Evans, Nicholas, Patino, Jose, Bonastre, Jean-François, Noé, Paul-Gauthier, Todisco, Massimiliano
The VoicePrivacy Challenge aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of
Externí odkaz:
http://arxiv.org/abs/2205.07123