000192607 001__ 192607
000192607 005__ 20190420173552.0
000192607 0247_ $$2doi$$a10.1109/TIFS.2011.2166387
000192607 02470 $$2ISI$$a000299440300011
000192607 037__ $$aARTICLE
000192607 245__ $$aA Fast Parts-based Approach to Speaker Verification using Boosted Slice Classifiers
000192607 269__ $$a2012
000192607 260__ $$c2012
000192607 336__ $$aJournal Articles
000192607 520__ $$aSpeaker verification on portable devices like smartphones is gradually becoming popular. In this context, two issues need to be considered: 1) such devices have relatively limited computation resources, and 2) they are liable to be used everywhere, possibly in very noisy, uncontrolled environments. This work aims to address both these issues by proposing a computationally efficient yet robust speaker verification system. This novel parts-based system draws inspiration from face and object detection systems in the computer vision domain. The system involves boosted ensembles of simple threshold-based classifiers. It uses a novel set of features extracted from speech spectra, called “slice features”. The performance of the proposed system was evaluated through extensive studies involving a wide range of experimental conditions using the TIMIT, HTIMIT and MOBIO corpus, against standard cepstral features and Gaussian Mixture Model-based speaker verification systems.
000192607 700__ $$0243369$$aRoy, Anindya$$g177638
000192607 700__ $$0243959$$aMagimai.-Doss, Mathew$$g127186
000192607 700__ $$0243994$$aMarcel, Sébastien$$g143942
000192607 773__ $$j7$$k1$$q241-254$$tIEEE Transactions on Information Forensics and Security
000192607 8564_ $$s816252$$uhttps://infoscience.epfl.ch/record/192607/files/Roy_IEEETRANS.IFS_2011.pdf$$yn/a$$zn/a
000192607 909C0 $$0252189$$pLIDIAP$$xU10381
000192607 909CO $$ooai:infoscience.tind.io:192607$$pSTI$$particle$$qGLOBAL_SET
000192607 917Z8 $$x148230
000192607 917Z8 $$x148230
000192607 937__ $$aEPFL-ARTICLE-192607
000192607 970__ $$aRoy_IEEETRANS.IFS_2011/LIDIAP
000192607 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000192607 980__ $$aARTICLE