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Recently, the hybrid deep neural networks and hidden Markov models (DNN/HMMs) have achieved dramatic gains over the conventional GMM/HMMs method on various large vocabulary continuous speech recognition (LVCSR) tasks. In this paper, we propose two new methods to further improve the hybrid DNN/HMMs model: i) use dropout as pre-conditioner (DAP) to initialize DNN prior to back-propagation (BP) for better recognition accuracy; ii) employ a shrinking DNN structure (sDNN) with hidden layersdoi:10.1109/icassp.2014.6854927 dblp:conf/icassp/ZhangBZ0D14 fatcat:ef3zuavnnbezrds7r6r3ppgyoe