Name
Title | ||
|---|---|---|
A one-class classification approach to generalised speaker verification spoofing countermeasures using local binary patterns |
Abstract | ||
|---|---|---|
The vulnerability of automatic speaker verification systems to spoofing is now well accepted. While recent work has shown the potential to develop countermeasures capable of detecting spoofed speech signals, existing solutions typically function well only for specific attacks on which they are optimised. Since the exact nature of spoofing attacks can never be known in practice, there is thus a need for generalised countermeasures which can detect previously unseen spoofing attacks. This paper presents a novel countermeasure based on the analysis of speech signals using local binary patterns followed by a one-class classification approach. The new countermeasure captures differences in the spectro-temporal texture of genuine and spoofed speech, but relies only on a model of the former. We report experiments with three different approaches to spoofing and with a state-of-the-art i-vector speaker verification system which uses probabilistic linear discriminant analysis for intersession compensation. While a support vector machine classifier is tuned with examples of converted voice, it delivers reliable detection of spoofing attacks using synthesized speech and artificial signals, attacks for which it is not optimised. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BTAS.2013.6712706 | Biometrics: Theory, Applications and Systems |
Keywords | Field | DocType |
signal classification,speaker recognition,speech processing,support vector machines,artificial signals,automatic speaker verification systems,converted voice,generalised speaker verification spoofing countermeasures,genuine speech,i-vector speaker verification system,intersession compensation,local binary patterns,one-class classification approach,probabilistic linear discriminant analysis,spectro-temporal texture,speech signals analysis,spoofed speech,spoofing attacks,support vector machine classifier,synthesized speech | Speech processing,One-class classification,Pattern recognition,Spoofing attack,Voice activity detection,Computer science,Support vector machine,Local binary patterns,Speech recognition,Speaker recognition,Speaker diarisation,Artificial intelligence | Conference |
Citations | PageRank | References |
33 | 1.29 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Federico Alegre | 1 | 93 | 4.74 |
| Asmaa Amehraye | 2 | 68 | 3.94 |
| nicholas evans | 3 | 594 | 54.41 |