Abstract

This paper presents novel variations of group sparse regularization techniques. We expand upon the Sparse Group LASSO formulation to incorporate different learning techniques for better sparsity enforcement within a group and demonstrate the effectiveness of the algorithms for spectral denoising with applications to robust Automatic Speech Recognition (ASR). In particular, we show that with a strategic selection of groupings greater robustness to noisy speech recognition can be achieved when compared to state-of-the-art techniques like the Fast Iterative Shrinkage Thresholding Algorithm (FISTA) implementation of the Sparse Group LASSO. Moreover, we demonstrate that group sparse regularization techniques can offer significant gains over efficient techniques like the Elastic Net. We also show that the proposed algorithms are effective in exploiting collinear dictionaries to deal with the inherent highly …

Date

December 7, 2011

Authors

Qun Feng Tan, Shrikanth S Narayanan

Journal

IEEE Transactions on Audio, Speech, and Language Processing

Volume

20

Issue

4

Pages

1337-1346

Publisher

IEEE