Abstract

Missing data techniques (MDTs) have been widely employed and shown to improve speech recognition results under noisy conditions. This paper presents a new technique which improves upon previously proposed sparse imputation techniques relying on the least absolute shrinkage and selection operator (LASSO). LASSO is widely employed in compressive sensing problems. However, the problem with LASSO is that it does not satisfy oracle properties in the event of a highly collinear dictionary, which happens with features extracted from most speech corpora. When we say that a variable selection procedure satisfies the oracle properties, we mean that it enjoys the same performance as though the underlying true model is known. Through experiments on the Aurora 2.0 noisy spoken digits database, we demonstrate that the Least Angle Regression implementation of the Elastic Net (LARS-EN) algorithm is …

Date

2011

Authors

Qun Feng Tan, Panayiotis G Georgiou, Shrikanth Narayanan

Journal

IEEE transactions on audio, speech, and language processing

Volume

19

Issue

8

Pages

2418-2429

Publisher

IEEE