Name
Abstract | ||
|---|---|---|
This paper presents a detailed description and analysis of a joint submission of Institute for Infocomm Research ((IR)-R-2) and National University of Singapore (NUS), which is the top performing system to AP16-OL7 Challenge. The submitted system was a fusion of two sub-systems: the i-vector system and GMM-SVM system, both based on state-of-the-art bottleneck feature. Central to our work presented in this paper is a language-dependent UBM GMM-SVM system and traditional i-vector polynomials expansion with SVM classifier. The FoCal toolkit was used for sub-system fusion. Experimental results show that the proposed approach achieves significant improvement over the baseline system on the development and evaluation sets. Our final submission achieve EER 0.440%, 1.09% and identification rates 98.9%, 97.6% on the development set and evaluation set, respectively. |
Year | Venue | Field |
|---|---|---|
2017 | Asia-Pacific Signal and Information Processing Association Annual Summit and Conference | Bottleneck,Mel-frequency cepstrum,Polynomial,Computer science,Support vector machine,Signal-to-noise ratio,Speech recognition,Language recognition,NIST,Baseline system |
DocType | ISSN | Citations |
Conference | 2309-9402 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanwu Sun | 1 | 98 | 14.15 |
| Kong-Aik Lee | 2 | 709 | 60.64 |
| Trung Hieu Nguyen | 3 | 44 | 7.08 |
| Bin Ma | 4 | 600 | 47.26 |
| Haizhou Li | 5 | 3678 | 334.61 |