  Supervised speech separation uses supervised learning algorithms to learn a
mapping from an input noisy signal to an output target. With the fast
development of deep learning, supervised separation has become the most
important direction in speech separation area in recent years. For the
supervised algorithm, training target has a significant impact on the
performance. Ideal ratio mask is a commonly used training target, which can
improve the speech intelligibility and quality of the separated speech.
However, it does not take into account the correlation between noise and clean
speech. In this paper, we use the optimal ratio mask as the training target of
the deep neural network (DNN) for speech separation. The experiments are
carried out under various noise environments and signal to noise ratio (SNR)
conditions. The results show that the optimal ratio mask outperforms other
training targets in general.
