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Artificial Neural Networks (ANNs) have experienced great success in the past few years. The increasing complexity of these models leads to less understanding about their decision processes. Therefore, introspection techniques have been proposed, mostly for images as input data. Patterns or relevant regions in images can be intuitively interpreted by a human observer. This is not the case for more complex data like speech recordings. In this work, we investigate the application of commondoi:10.18653/v1/w18-5421 dblp:conf/emnlp/KrugS18 fatcat:jv5pl3qptrebbix2kriz733z3m