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Robustness of automatic speech recognition (ASR) systems is a critical issue due to noise and reverberations. Speech enhancement and model adaptation have been studied for long time to address this issue. Recently, the developments of multitask joint-learning scheme that addresses noise reduction and ASR criteria in a unified modeling framework show promising improvements, but the model training highly relies on paired clean-noisy data. To overcome this limit, the generative adversarialdoi:10.21437/interspeech.2019-2078 dblp:conf/interspeech/ZhaoNTM19 fatcat:3bfy4hfeybgrtfqjo5buk4f43q