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Depositordc.contributorYamagishi, Junichi
Funderdc.contributor.otherEuropean Commissionen_UK
Funderdc.contributor.otherAcademy of Finlanden_UK
Funderdc.contributor.otherJapan Society for the Promotion of Science (JSPS)
Data Creatordc.creatorKinnunen, Tomi
Data Creatordc.creatorSahidullah, Md
Data Creatordc.creatorDelgado, Héctor
Data Creatordc.creatorTodisco, Massimiliano
Data Creatordc.creatorEvans, Nicholas
Data Creatordc.creatorYamagishi, Junichi
Data Creatordc.creatorLee, Kong Aik
Date Accessioneddc.date.accessioned2017-07-26T16:01:44Z
Date Availabledc.date.available2017-08-18T04:15:19Z
Citationdc.identifier.citationKinnunen, Tomi; Sahidullah, Md; Delgado, Héctor; Todisco, Massimiliano; Evans, Nicholas; Yamagishi, Junichi; Lee, Kong Aik. (2017). The 2nd Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017) Database, [sound]. University of Edinburgh. The Centre for Speech Technology Research (CSTR). https://doi.org/10.7488/ds/2105.en
Persistent Identifierdc.identifier.urihttp://hdl.handle.net/10283/2778
Persistent Identifierdc.identifier.urihttps://doi.org/10.7488/ds/2105
Dataset Description (abstract)dc.description.abstract## This item has been replaced by the one which can be found at https://doi.org/10.7488/ds/2332 ## This is a database used for the Second Automatic Speaker Verification Spoofing and Countermeasuers Challenge, for short, ASVspoof 2017 (http://www.asvspoof.org) organized by Tomi Kinnunen, Md Sahidullah, Héctor Delgado, Massimiliano Todisco, Nicholas Evans, Junichi Yamagishi, Kong Aik Lee in 2017. The ASVspoof challenge aims to encourage further progress through (i) the collection and distribution of a standard dataset with varying spoofing attacks implemented with multiple, diverse algorithms and (ii) a series of competitive evaluations for automatic speaker verification. The ASVspoof 2017 challenge follows on from two special sessions on spoofing and countermeasures for automatic speaker verification held during INTERSPEECH 2013 and 2015. While the first edition in 2013 was targeted mainly at increasing awareness of the spoofing problem, the 2015 edition included a first challenge on the topic, with commonly defined evaluation data, metrics and protocols. The task in ASVspoof 2015 was to discriminate genuine human speech from speech produced using text-to-speech (TTS) and voice conversion (VC) attacks. The challenge was drawn upon state-of-the-art TTS and VC attacks data prepared for the “SAS” corpus by TTS and VC researchers. The primary technical goal of ASVspoof 2017 is to assess spoofing attack detection accuracy with ‘out in the wild’ conditions, thereby advancing research towards generalized spoofing countermeasure, in particular to detect replay. In addition, ASVspoof 2017 attempts to better interlink the research efforts from spoofing and text-dependent ASV communities. To this end, ASVspoof 2017 makes an extensive use of the recent text-dependent RedDots corpus, as well as a replayed version of the same data. The ASVspoof 2017 database contains large amount of speech data collected from 179 replay sessions in 125 unique replay configurations. Number of speakers is 42. A replay configuration means a unique combination of room, replay device and recording device, while a session refers to a set of source files, which share the same replay configuration.en_UK
Dataset Description (TOC)dc.description.tableofcontentsBelow are some details about the database: 1. Training and development data are included in 'ASVspoof2017_train.zip’ ’ASVspoof2017_dev.zip’. Training dataset contains audio files with known ground-truth which can be used to train systems which can distinguish between genuine and spoofed speech. The development dataset contains audio files with known ground-truth which can be used for the development of spoofing detection algorithms. 2. Evaluation data is available in 'ASVspoof2017_eval.zip’. 3. Protocol and keys are available in 'protocol.zip’. 4. Additional Instruction.txt file is included in packages. There are originally used for the challenge participant to explain the database. 5. About how to compute the EERs, please refer the evaluation plan included in this repository. 6. To compare with the challenge results, please refer the summary paper of the challenge included in this repository. 7. The baseline results based on CQCC can be reproduced using publicly released Matlab-based implementation of a replay attack spoofing detector http://www.asvspoof.org/data2017/baseline_CM.zip 8. Summary and details of participant’s systems submitted to ASVspoof 2017 are available in ‘submitted-system-descriptions.zip’.en_UK
Languagedc.language.isoengen_UK
Publisherdc.publisherUniversity of Edinburgh. The Centre for Speech Technology Research (CSTR)en_UK
Relation (Is Version Of)dc.relation.isversionofhttps://doi.org/10.7488/ds/298en_UK
Superseded Bydc.relation.isreplacedbyhttps://doi.org/10.7488/ds/2332
Rightsdc.rightsYou are free to use this database under Creative Commons Attribution-NonCommercial License (CC-BY-NC). Regarding Creative Commons License: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), please see https://creativecommons.org/licenses/by-nc/4.0/en
Subjectdc.subjectspeechen_UK
Subjectdc.subjectspeaker verificationen
Subjectdc.subjectanti-spoofingen
Subjectdc.subjectreplyen
Titledc.titleSUPERSEDED - The 2nd Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017) Databaseen_UK
Typedc.typesounden_UK

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