This　paper　presents　a　novel　approach　for　estimating　auto-regressive　(AR)　model　parameters　using　deep　neural　network　(DNN)　in　the　AR-Wiener　filtering　speech　enhancement.　Unlike　conventional　DNN　that　predicts　one　kind　of　target,　the　DNN　used　in　this　paper　is　trained　to　predict　the　AR　model　parameters　of　speech　and　noise　simultaneously　at　offline　stage.　We　train　this　network　by　minimizing　the　Euclidean　distance　between　the　output　of　DNN　and　the　AR　model　parameters　of　clean　speech　and　noise.　At　online　stage,　the　acoustic　features　are　first　extracted　from　noisy　speech　as　the　input　of　the　DNN.　Then,　AR　model　parameters　of　speech　and　noise　are　estimated　by　the　DNN　simultaneously.　Finally,　the　Wiener　filter　is　constructed　by　the　AR　model　parameters　of　speech　and　noise.　However,　the　AR　model　parameters　only　models　the　spectral　shape　not　the　spectral　details,　there　are　still　some　residual　noise　between　the　harmonics.　In　order　to　solve　this　problem,　we　introduce　the　speech-presence　probability　(SPP),　that　is,　in　the　test　stage,　the　SPP　is　estimated　and　is　used　to　update　the　Wiener　filter.　The　experimental　results　show　that　our　approach　has　higher　performance　compared　with　some　existing　approaches.