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The traditional generalized sidelobe canceller (GSC) is a common speech enhancement front end to improve the noise robustness of automatic speech recognition (ASR) systems in the far-field cases. However, the traditional GSC is optimized based on the signal level criteria, causing it not to guarantee the optimal ASR performance. To address this issue, we propose a novel dual-channel deep neural network (DNN)-based GSC structure, called nnGSC, which is optimized by using the objective of maximizing the ASR performance. Our key idea is to ma
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