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 [BibTeX] [Marc21]
Model-based Sparse Component Analysis for Multiparty Distant Speech Recognition
Type of publication: Thesis
Citation: Asaei_THESIS_2013
Year: 2013
School: École Polytechnique Fédérale de Lausanne
Abstract: This 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.
Projects Idiap
FP 7
Authors Asaei, Afsaneh
Added by: [UNK]
Total mark: 0
  • Asaei_THESIS_2013.pdf