Name
Abstract | ||
|---|---|---|
It is well known that speaker verification systems are subject to spoofing attacks. The Automatic Speaker Verification Spoofing and Countermeasures Challenge -- ASVSpoof2015 -- provides a standard spoofing database, containing attacks based on synthetic speech, along with a protocol for experiments. This paper describes CPqD's systems submitted to the ASVSpoof2015 Challenge, based on deep neural networks, working both as a classifier and as a feature extraction module for a GMM and a SVM classifier. Results show the validity of this approach, achieving less than 0.5\% EER for known attacks. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Speaker verification,Spoofing attack,Pattern recognition,Computer science,Speech recognition,Feature extraction,Artificial intelligence,Svm classifier,Deep learning,Classifier (linguistics),Machine learning,Deep neural networks |
DocType | Volume | Citations |
Journal | abs/1508.01746 | 1 |
PageRank | References | Authors |
0.35 | 12 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| alan godoy | 1 | 1 | 0.35 |
| flavio olmos simoes | 2 | 28 | 2.66 |
| jose augusto stuchi | 3 | 3 | 1.06 |
| Marcus A. Angeloni | 4 | 47 | 3.59 |
| mario uliani | 5 | 1 | 0.35 |
| R. Violato | 6 | 24 | 1.52 |