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• W3149804260 abstract "Voice anti-spoofing aims at classifying a given utterance either as a bonafide human sample, or a spoofing attack (e.g. synthetic or replayed sample). Many anti-spoofing methods have been proposed but most of them fail to generalize across domains (corpora) -- and we do not know emph{why}. We outline a novel interpretative framework for gauging the impact of data quality upon anti-spoofing performance. Our within- and between-domain experiments pool data from seven public corpora and three anti-spoofing methods based on Gaussian mixture and convolutive neural network models. We assess the impacts of long-term spectral information, speaker population (through x-vector speaker embeddings), signal-to-noise ratio, and selected voice quality features." @default.
• W3149804260 created "2021-04-13" @default.
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• W3149804260 date "2021-03-26" @default.
• W3149804260 modified "2023-09-27" @default.
• W3149804260 title "Data Quality as Predictor of Voice Anti-Spoofing Generalization" @default.
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