Date of Original Version



Conference Proceeding

Journal Title

Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)

First Page


Last Page


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Abstract or Description

As a feed-forward architecture, the recently proposed maxout networks integrate dropout naturally and show state-of-the-art results on various computer vision datasets. This paper investigates the application of deep maxout networks (DMNs) to large vocabulary continuous speech recognition (LVCSR) tasks. Our focus is on the particular advantage of DMNs under low-resource conditions with limited transcribed speech. We extend DMNs to hybrid and bottleneck feature systems, and explore optimal network structures (number of maxout layers, pooling strategy, etc) for both setups. On the newly released Babel corpus, behaviors of DMNs are extensively studied under different levels of data availability. Experiments show that DMNs improve low-resource speech recognition significantly. Moreover, DMNs introduce sparsity to their hidden activations and thus can act as sparse feature extractors.





Published In

Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), 398-403.