In　this　paper,　we　present　three　strategies　to　achieve　speech　enhancement,　which　is　based　on　Cepstral　Mapping　and　Deep　Neural　Networks　(DNN).　Firstly,　we　apply　DNN　to　directly　predict　the　clean　speech　Cepstral　feature　given　noisy　Cepstral　input.　Then,　by　waveform　reconstruction,　we　can　obtain　desired　clean　speech.　Comparing　with　the　method　of　directly　mapping　log-power　spectral　(LPS),　our　method　is　able　to　be　more　effective　to　recover　speech　harmonic　structure　and　gain　the　higher　speech　quality.　Additionally,　we　also　utilize　DNN　to　estimate　ideal　Wiener　filter　by　giving　noisy　Cepstral　input.　Finally,　a　fusion　framework　is　proposed　to　acquire　enhanced　speech　signal,　which　combines　Cepstral　feature　mapping　and　Wiener　filter.　Experiments　show　that　the　proposed　algorithms　are　able　to　achieve　the　state-of-the-art　performance　in　improving　the　quality　and　intelligibility　of　noisy　speech.　©　2018　IEEE.