Back to Search
Start Over
Study on Feature Complementarity of Statistics, Energy, and Principal Information for Spoofing Detection
- Source :
- IEEE Access, Vol 8, Pp 141170-141181 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Conventional speaker verification systems become frail or incompetent while facing attack from spoofed speech. Presently many anti-spoofing countermeasures have been studied for automatic speaker verification. It has been known that the salient feature is of a more important role rather than the selection of classifiers in the current research field of spoofing detection. The effectiveness of constant-Q transform (CQT) has been demonstrated for anti-spoofing feature analysis in many research literatures on automatic speaker verification. On the basis of CQT-based information sub-features, i.e. octave-band principal information (OPI), full-band principal information (FPI), short-term spectral statistics information (STSSI) and magnitude-phase energy information (MPEI), three concatenated features are proposed by investigating their information complementarity in this paper, the first one is constant-Q statistics-plus-principal information coefficients (CQSPIC) by combining OPI, FPI and STSSI; the second one is constant-Q energy-plus-principal information coefficients (CQEPIC) by combining OPI, FPI and MPEI and the third one is constant-Q energy-statistics-principal information coefficients (CESPIC) by combining OPI, FPI, MPEI and STSSI. In this paper, we set up deep neural network (DNN) classifiers for evaluation of the proposed features. Experiments show that the proposed features can outperform some commonly used features meanwhile the proposed systems give better or comparable performance comparing with state-of-the-art performance on ASVspoof 2019 logical access and physical access corpus.
- Subjects :
- anti-spoofing countermeasure
Spoofing attack
General Computer Science
Artificial neural network
business.industry
Computer science
Feature extraction
General Engineering
Speech synthesis
Pattern recognition
computer.software_genre
Complementarity (physics)
Salient
General Materials Science
Artificial intelligence
Mel-frequency cepstrum
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
computer
Constant-Q transform
lcsh:TK1-9971
automatic speaker verification
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....3cfddf81e6fc92a2d7f2138d4f675dd1