University of Illinois Urbana-Champaign

Advances in Sparse Classification

Bharadwaj, Sujeeth

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Description

Title
Advances in Sparse Classification
Author(s)
Bharadwaj, Sujeeth
Contributor(s)
Hasegawa-Johnson, Mark
Issue Date
2009-12
Keyword(s)
  • compressed sensing
  • speech recognition
  • nonparametric speech recognition
  • sparse classification
  • sparse projection
Abstract
A recent result in compressed sensing (CS) allows us to perform non-parametric speech recognition that is robust to noise, and that requires few training examples. By taking fixed length representations of training samples and stacking them in a matrix, we form a frame, or an over-complete basis. Gemmeke and Cranen have shown that sparse projections onto this frame recover the correct transcription with 91% accuracy at -5 dB SNR. We propose that the goal of speech recognition is not sparse projection onto training tokens, but onto training types. Sparse projection onto types can be achieved by building a frame for each word in the dictionary, and stacking the frames to form a rank 3 tensor. Speech recognition is performed by convex linear projection onto the tensor, with sparsity enforced only in the index that specifies type. We derive a mixed L1/L2 relaxation that can be globally optimized using Newton descent.
Type of Resource
text
Language
en
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