Name
Abstract | ||
|---|---|---|
Sparse representations of signals have received a great deal of attention in recent years, and the sparse representation classifier has very lately appeared in a speaker recognition system. This approach represents the (sparse) GMM mean supervector of an unknown speaker as a linear combination of an over-complete dictionary of GMM supervectors of many speaker models, and ℓ1-norm minimization results in a non-zero coefficient corresponding to the unknown speaker class index. Here this approach is tested on large databases, introducing channel-/session-variability compensation, and fused with a GMM-SVM system. Evaluations on the NIST 2001 SRE and NIST 2006 SRE database show that when the outputs of the MFCC UBM-GMM based classifier (for NIST 2001 SRE) or MFCC GMM-SVM based classifier (for NIST 2006 SRE) are fused with the MFCC GMM Sparse Representation Classifier (GMM-SRC) based classifier, an absolute gain of 1.27% and 0.25% in EER can be achieved respectively. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICASSP.2011.5947366 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
speaker recognition,ℓ1-norm minimization,GMM mean supervector,MFCC GMM-sparse representation classifier,UBM-GMM based classifier,sparse representation classification,speaker models,speaker verification,compressive sensing,sparse representation,speaker verification | Mel-frequency cepstrum,Linear combination,Pattern recognition,Computer science,Sparse approximation,Speech recognition,Feature extraction,NIST,Speaker recognition,Artificial intelligence,Classifier (linguistics),Compressed sensing | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4577-0537-3 | 978-1-4577-0537-3 | 25 |
PageRank | References | Authors |
0.88 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia Min Karen Kua | 1 | 30 | 1.63 |
| Eliathamby Ambikairajah | 2 | 493 | 64.55 |
| Julien Epps | 3 | 1466 | 105.10 |
| Roberto Togneri | 4 | 814 | 48.33 |