... | ... | @@ -56,12 +56,12 @@ The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meet |
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This face database was created by Aleix Martinez and Robert Benavente in the Computer Vision Center (CVC) at the U.A.B. It contains over 4,000 color images corresponding to 126 people's faces (70 men and 56 women). Images feature frontal view faces with different facial expressions, illumination conditions, and occlusions (sun glasses and scarf). The pictures were taken at the CVC under strictly controlled conditions. No restrictions on wear (clothes, glasses, etc.), make-up, hair style, etc. were imposed to participants. Each person participated in two sessions, separated by two weeks (14 days) time. The same pictures were taken in both sessions.
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### asvspoof
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`Repository: /idiap/resource/database/AVSpoof/`
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`Repository: /idiap/resource/database/ASVspoof/`
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The database has been used in the first Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2015). Genuine speech is collected from 106 speakers (45 male, 61 female) and with no significant channel or background noise effects. Spoofed speech is generated from the genuine data using a number of different spoofing algorithms, including variations of speech synthesis and voice conversion algorithms. The full dataset is partitioned into three subsets, the first for training, the second for development and the third for evaluation. The database has 5 known attacks (both development and evaluation sets) and 5 unknown attacks (only in evaluation set). The database does not contain any replay attacks, so all attacks are so called `logical access` attacks.
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### avspoof
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`Repository: /idiap/resource/database/ASVspoof/`
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`Repository: /idiap/resource/database/AVSpoof/`
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The AVspoof database provides speech-based spoofing attacks to test both ASV systems and anti-spoofing algorithms. The attacks are created based on audio recordings acquired from 31 male and 13 female participants. The data acquisition process lasted approximately two months, spanned several sessions, which were configured in different environmental conditions and setups. After the collection of the data, replay (with iPhone 3GS, Samsung Galaxy 4, and a laptop), voice conversion (also replayed with laptop and high quality speakers), and speech synthesis (also replayed with laptop and high quality speakers) attacks were generated. Therefore, the database has both so called `logical access` attacks and `presentation attacks`.
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