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Acoustic models for state-of-the-art DNN-based speech recognition systems are typically trained using at least several hundred hours of task-specific training data. However, this amount of training data is not always available for some applications. In this paper, we investigate how to use an adult speech corpus to improve DNN-based automatic speech recognition for non-native children's speech. Although there are many acoustic and linguistic mismatches between the speech of adults and children,doi:10.21437/wocci.2016-7 dblp:conf/wocci/QianWES16 fatcat:rw46atb4ubhsxmb3cbdzqxld3i