Abstract / truncated to 115 words (read the full abstract)

Speech recognition performance degrades in the presence of background noise. In this thesis, several methods are developed to improve the noise robustness. Most of the work pertains to the use of sparse representations of speech: speech segments are described as a sparse linear combination of example speech segments, exemplars. Using techniques from missing data theory and compressed sensing, it is proposed to find, for each noisy speech observation, a sparse linear combination of exemplars using only speech features that are not corrupted by noise. This linear combination of clean speech exemplars is then used to reconstruct and estimate of the clean speech. Later in the thesis, it is proposed to augment this model by expressing ... toggle 6 keywords

speech recognition missing data noise robustness compressed sensing sparse representations exemplar-based

Information

Author
Gemmeke, Jort
Institution
Radboud University Nijmegen
Supervisors
Publication Year
2011
Upload Date
July 20, 2011

First few pages / click to enlarge

The current layout is optimized for mobile phones. Page previews, thumbnails, and full abstracts will remain hidden until the browser window grows in width.

The current layout is optimized for tablet devices. Page previews and some thumbnails will remain hidden until the browser window grows in width.