Name
Abstract | ||
|---|---|---|
Nowadays spoofing detection is one of the priority research areas in the field of automatic speaker verification. The success of Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof) Challenge 2015 confirmed the impressive perspective in detection of unforeseen spoofing trials based on speech synthesis and voice conversion techniques. However, there is a small number of researches addressed to replay spoofing attacks which arc more likely to be used by non-professional impersonators. This paper describes the Speech Technology Center (STC) anti-spoofing system submitted for ASVspoof 2017 which is focused on replay attacks detection. Here we investigate the efficiency of a deep learning approach for solution of the mentioned-above task. Experimental results obtained on the Challenge corpora demonstrate that the selected approach outperforms current state-of-the-art baseline systems in terms of spoofing detection quality. Our primary system produced an EER of 6.73% on the evaluation part of the corpora which is 72% relative improvement over the ASVspoof 2017 baseline system. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.21437/Interspeech.2017-360 | 18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION |
Keywords | Field | DocType |
spoofing, anti-spoofing, speaker recognition, replay attack detection, CNN, RNN, ASVspoof | Computer science,Computer network,Speech recognition,Human–computer interaction,Artificial intelligence,Deep learning,Replay attack | Conference |
ISSN | Citations | PageRank |
2308-457X | 13 | 0.65 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Galina Lavrentyeva | 1 | 24 | 7.40 |
| Sergey Novoselov | 2 | 51 | 10.57 |
| Egor Malykh | 3 | 13 | 0.99 |
| Alexander Kozlov | 4 | 37 | 6.27 |
| Oleg Kudashev | 5 | 47 | 5.91 |
| Vadim Shchemelinin | 6 | 26 | 4.56 |