10.1184/R1/6473306.v1 Yajie Miao Yajie Miao Florian Metze Florian Metze Shourabh Rawat Shourabh Rawat Deep maxout networks for low-resource speech recognition Carnegie Mellon University 2013 Deep maxout networks speech recognition low-resource conditions deep learning 2013-12-01 00:00:00 Journal contribution https://kilthub.cmu.edu/articles/journal_contribution/Deep_maxout_networks_for_low-resource_speech_recognition/6473306 <p>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.</p>