ASVspoof 2019: Future horizons in spoofed and fake audio detection

Todisco, Massimiliano; Wang, Xin; Vestman, Ville; Sahidullah, Md; Delgado, Hector; Nautsch, Andreas; Yamagishi, Junichi; Evans, Nicholas; Kinnunen, Tomi; Aik Lee, Kong
Submitted on ArXiv, 9 April 2019

ASV spoof, now in its third edition, is a series of community-led challenges which promote the development of countermeasures to protect automatic speaker verification (ASV) from the threat of spoofing. Advances in the 2019 edition include: (i) a consideration of both logical access (LA) and physical access (PA) scenarios and the three major forms of spoofing attack, namely synthetic, converted and replayed speech; (ii) spoofing attacks generated with state-of-the-art neural acoustic and waveform models; (iii) an improved, controlled simulation of replay attacks; (iv) use of the tandem detection cost function (t-DCF) that reflects the impact of both spoofing and countermeasures upon ASV reliability. Even if ASV remains the core focus, in retaining the equal error rate (EER) as a secondary metric, ASYspoof also embraces the growing importance of fake audio detection. ASVspoof 2019 attracted the participation of 63 research teams, with more than half of these reporting systems that improve upon the performance of two baseline spoofing countermeasures. This paper describes the 2019 database, protocols and challenge results. It also outlines major findings which demonstrate the real progress made in protecting against the threat of spoofing and fake audio. Index Terms: spoofing, automatic speaker verification, ASVspoof, presentation attack detection, fake audio.  

Digital Security
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