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Data augmentation using label preserving transformations has been shown to be effective for neural network training to make invariant predictions. In this paper we focus on data augmentation approaches to acoustic modeling using deep neural networks (DNNs) for automatic speech recognition (ASR). We first investigate a modified version of a previously studied approach using vocal tract length perturbation (VTLP) and then propose a novel data augmentation approach based on stochastic featuredoi:10.1109/taslp.2015.2438544 fatcat:bbstnbnzivernj35kymyk5tlrq