1,721,120 research outputs found
Integrated spoofing countermeasures and automatic speaker verification: an evaluation on ASVspoof 2015
No full textASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge
No full textFurther optimisations of constant Q cepstral processing for integrated utterance verification and text-dependent speaker verification
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From media fears to research reality: How ready are countermeasures against speaker verification spoofing?
No full textImpact of bandwidth and channel variation on presentation attack detection for speaker verification
No full textThe ASVspoof 2017 Challenge: Assessing the Limits of Replay Spoofing Attack Detection
Full text linkThe ASVspoof initiative was created to promote the development of countermeasures which aim to protect automatic speaker verification (ASV) from spoofing attacks. The first community-led, common evaluation held in 2015 focused on countermeasures for speech synthesis and voice conversion spoofing attacks. Arguably, however, it is replay attacks which pose the greatest threat. Such attacks involve the replay of recordings collected from enrolled speakers in order to provoke false alarms and can be mounted with greater ease using everyday consumer devices. ASVspoof 2017, the second in the series, hence focused on the development of replay attack countermeasures. This paper describes the database, protocols and initial findings. The evaluation entailed highly heterogeneous acoustic recording and replay conditions which increased the equal error rate (EER) of a baseline ASV system from 1.76% to 30.71%. Submissions were received from 49 research teams, 20 of which improved upon a baseline replay spoofing detector EER of 24.65%, in terms of replay/non-replay discrimination. While largely successful, the evaluation indicates that the quest for countermeasures which are resilient in the face of variable replay attacks remains very much alive
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The 2nd Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017) Database
No full textThis 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.Kinnunen, Tomi; Sahidullah, Md; Delgado, Héctor; Todisco, Massimiliano; Evans, Nicholas; Yamagishi, Junichi; Lee, Kong Aik. (2018). The 2nd Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017) Database, Version 2, [sound]. University of Edinburgh. The Centre for Speech Technology Research (CSTR). http://dx.doi.org/10.7488/ds/2332
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