  Deep neural network (DNN)-based speech enhancement usually uses a clean
speech as a training target. However, it is hard to collect large amounts of
clean speech because the recording is very costly. In other words, the
performance of current speech enhancement has been limited by the amount of
training data. To relax this limitation, Noisy-target Training (NyTT) that
utilizes noisy speech as a training target has been proposed. Although it has
been experimentally shown that NyTT can train a DNN without clean speech, a
detailed analysis has not been conducted and its behavior has not been
understood well. In this paper, we conduct various analyses to deepen our
understanding of NyTT. In addition, based on the property of NyTT, we propose a
refined method that is comparable to the method using clean speech.
Furthermore, we show that we can improve the performance by using a huge amount
of noisy speech with clean speech.
