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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sturim, D. Campbell, W. Dehak, N. Karam, Z. McCree, A. Reynolds, D. Richardson, F. Torres-Carrasquillo, P. Shum, S. |
| Copyright Year | 2011 |
| Description | Author affiliation: MIT Lincoln Laboratory, USA (Sturim, D.; Campbell, W.; Karam, Z.; McCree, A.; Reynolds, D.; Richardson, F.; Torres-Carrasquillo, P.) || MIT CSAIL, USA (Dehak, N.; Shum, S.) |
| Abstract | Research in the speaker recognition community has continued to address methods of mitigating variational nuisances. Telephone and auxiliary-microphone recorded speech emphasize the need for a robust way of dealing with unwanted variation. The design of recent 2010 NIST-SRE Speaker Recognition Evaluation (SRE) reflects this research emphasis. In this paper, we present the MIT submission applied to the tasks of the 2010 NIST-SRE with two main goals—language-independent scalable modeling and robust nuisance mitigation. For modeling, exclusive use of inner product-based and cepstral systems produced a language-independent computationally-scalable system. For robustness, systems that captured spectral and prosodic information, modeled nuisance subspaces using multiple novel methods, and fused scores of multiple systems were implemented. The performance of the system is presented on a subset of the NIST SRE 2010 core tasks. |
| Starting Page | 5272 |
| Ending Page | 5275 |
| File Size | 227291 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457705380 |
| ISSN | 15206149 |
| e-ISBN | 9781457705397 |
| e-ISBN | 9781457705373 |
| DOI | 10.1109/ICASSP.2011.5947547 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-05-22 |
| Publisher Place | Czech Republic |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Speech NIST Microphones Covariance matrix Speaker recognition Training Interviews |
| Content Type | Text |
| Resource Type | Article |
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