Deep　neural　networks　(DNN)　with　skip　connections,　which　is　different　from　the　standard　feed　forward　network　architecture,　is　added　skip　connections　between　networks.　By　adding　skip　connections　to　all　layers　of　neural　networks,　the　problem　of　gradient　vanishing　can　be　solved,　which　is　beneficial　for　training　deep　networks　and　makes　a　faster　convergence.　In　addition,　more　speech　signal　details　are　able　to　be　passed　by　the　skip　connections,　which　helps　the　network　to　better　recover　the　speech　signal.　In　our　paper,　firstly,　the　ideal　Wiener　filter　is　chosen　as　the　training　target　of　DNN　with　skip　connections　(Skip-DNN)　given　the　cepstral　feature　of　noisy　speech　signal　as　its　input.　Then,　we　investigate　the　enhanced　speech　performance　that　combines　the　DNN-based　phase　estimation　in　complex　domain　with　the　estimated　clean　speech　magnitude　by　using　the　ideal　Wiener　filter　and　Skip-DNN.　The　experiments　are　conducted　by　using　the　TIMIT　corpus　with　102　types　of　noises　at　four　different　signal　to　noise　ratio　(SNR)　levels.　According　to　the　experiments,　our　proposed　methods　are　able　to　achieve　the　higher　speech　quality　and　intelligibility　than　those　reference　approaches.