Parallel and Hierarchical Decision Making for Sparse Coding in Speech Recognition

Sparse coding exhibits promising performance in speech processing, mainly due to the large number of bases that can be used to represent speech signals. However, the high demand for computational power represents a major obstacle in the case of large datasets, as does the difficulty in utilising information scattered sparsely in high dimensional features. This paper reports the use of an online dictionary learning technique, proposed recently by the machine learning community, to learn large scale bases efficiently, and proposes a new parallel and hierarchical architecture to make use of the sparse information in high dimensional features. The approach uses multilayer perceptrons (MLPs) to model sparse feature subspaces and make local decisions accordingly; the latter are integrated by additional MLPs in a hierarchical way for making global decisions. Experiments on the WSJ database show that the proposed approach not only solves the problem of prohibitive computation with large-dimensional sparse features, but also provides better performance in a frame-level phone prediction task.