In　this　paper,　a　novel　approach　is　presented　to　predict　a　training　target　called　NMF-based　Wiener　filter　using　deep　neural　networks　(DNN)　in　the　nonnegative　matrix　factorization　(NMF)　based　speech　enhancement.　The　NMF-based　Wiener　filter,　as　a　masking-based　target,　is　easier　than　the　encoding　vectors　used　in　previous　algorithms　for　parameter　estimation.　The　intermediate　error　of　the　NMF-based　speech　enhancement　process　was　reduced　due　to　direct　prediction　of　the　NMF-based　Wiener　filter.　The　encoding　vectors　of　noisy　speech　were　extracted　with　the　NMF　algorithm　and　normalized　to　obtain　more　discriminative　input　features.　The　DNN　was　trained　to　learn　a　nonlinear　mapping　from　the　encoding　vector　of　noisy　speech　to　the　NMF-based　Wiener　filter.　At　test　stage,　the　predicted　NMF-based　Wiener　filter　was　used　to　enhance　noisy　speech.　The　objective　evaluations　demonstrated　that　the　proposed　algorithm　outperforms　some　existing　NMF-based　and　DNN-based　methods　at　various　input　signal-to-noise　ratio　(SNR)　levels.