000192326 001__ 192326
000192326 005__ 20190316235756.0
000192326 037__ $$aTHESIS
000192326 245__ $$aModel-based Sparse Component Analysis for Multiparty Distant Speech Recognition
000192326 269__ $$a2013
000192326 260__ $$bÉcole Polytechnique Fédérale de Lausanne$$c2013
000192326 336__ $$aTheses
000192326 520__ $$aThis research takes place in the general context of improving the performance of the Distant Speech Recognition (DSR) systems, tackling the reverberation and recognition of overlap speech. Perceptual modeling indicates that sparse representation exists in the auditory cortex. The present project thus builds upon the hypothesis that incorporating this information in DSR front-end processing could improve the speech recognition performance in realistic conditions including overlap and reverberation. More specifically, the goal of my PhD thesis is to exploit blind (source) separation of the speech components in a sparse space, also referred to as sparse component analysis (SCA), for multi-party multi-channel speech recognition.
000192326 700__ $$g188259$$aAsaei, Afsaneh$$0243353
000192326 8564_ $$uhttps://infoscience.epfl.ch/record/192326/files/Asaei_THESIS_2013.pdf$$zn/a$$s2940451$$yn/a
000192326 909C0 $$xU10381$$0252189$$pLIDIAP
000192326 909CO $$qGLOBAL_SET$$pSTI$$ooai:infoscience.tind.io:192326
000192326 970__ $$aAsaei_THESIS_2013/LIDIAP
000192326 973__ $$aEPFL
000192326 980__ $$aTHESIS