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pro vyhledávání: '"Mallinson, P."'
This paper evaluates the impact of training undergraduate students to improve their audio deepfake discernment ability by listening for expert-defined linguistic features. Such features have been shown to improve performance of AI algorithms; here, w
Externí odkaz:
http://arxiv.org/abs/2411.14586
This perspective calls for scholars across disciplines to address the challenge of audio deepfake detection and discernment through an interdisciplinary lens across Artificial Intelligence methods and linguistics. With an avalanche of tools for the g
Externí odkaz:
http://arxiv.org/abs/2411.05969
Spoofed audio, i.e. audio that is manipulated or AI-generated deepfake audio, is difficult to detect when only using acoustic features. Some recent innovative work involving AI-spoofed audio detection models augmented with phonetic and phonological f
Externí odkaz:
http://arxiv.org/abs/2410.15577
Autor:
Khanjani, Zahra, Ale, Tolulope, Wang, Jianwu, Davis, Lavon, Mallinson, Christine, Janeja, Vandana P.
Several types of spoofed audio, such as mimicry, replay attacks, and deepfakes, have created societal challenges to information integrity. Recently, researchers have worked with sociolinguistics experts to label spoofed audio samples with Expert Defi
Externí odkaz:
http://arxiv.org/abs/2409.06033
Background: In the absence of prospective data on diabetic foot ulcers (DFU), cross-sectional associations with causal risk factors (peripheral neuropathy, and peripheral arterial disease (PAD)) could be used to establish the validity of plantar ther
Externí odkaz:
http://arxiv.org/abs/2407.04676
Autor:
Richemond, Pierre Harvey, Tang, Yunhao, Guo, Daniel, Calandriello, Daniele, Azar, Mohammad Gheshlaghi, Rafailov, Rafael, Pires, Bernardo Avila, Tarassov, Eugene, Spangher, Lucas, Ellsworth, Will, Severyn, Aliaksei, Mallinson, Jonathan, Shani, Lior, Shamir, Gil, Joshi, Rishabh, Liu, Tianqi, Munos, Remi, Piot, Bilal
The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from preference data. This involves building datasets where each element is
Externí odkaz:
http://arxiv.org/abs/2405.19107
Autor:
Costin-Valentin Oancea
Publikováno v:
Bucharest Working Papers in Linguistics, Vol XX, Iss 2, Pp 117-121 (2018)
Christine Mallinson, Becky Childs, Gerard van Herk (eds.) Data Collection in Sociolinguistics, reviewed by Costin Oancea
Externí odkaz:
https://doaj.org/article/b46e8e3a35044e0ba5ad9dbfb097acd4
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Akademický článek
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Rapidly growing datasets from stellar spectroscopic surveys are providing unprecedented opportunities to analyse the chemical evolution history of our Galaxy. However, spectral analysis requires accurate modelling of synthetic stellar spectra for lat
Externí odkaz:
http://arxiv.org/abs/2403.19304