  This paper addresses the problem of automatic speech recognition (ASR) of a
target speaker in background speech. The novelty of our approach is that we
focus on a wakeup keyword, which is usually used for activating ASR systems
like smart speakers. The proposed method firstly utilizes a DNN-based mask
estimator to separate the mixture signal into the keyword signal uttered by the
target speaker and the remaining background speech. Then the separated signals
are used for calculating a beamforming filter to enhance the subsequent
utterances from the target speaker. Experimental evaluations show that the
trained DNN-based mask can selectively separate the keyword and background
speech from the mixture signal. The effectiveness of the proposed method is
also verified with Japanese ASR experiments, and we confirm that the character
error rates are significantly improved by the proposed method for both
simulated and real recorded test sets.
