This　paper　presents　a　novel　approach　for　estimating　auto-regressive　parameters　of　speech　and　noise　in　the　codebook-driven　Wiener　filtering　speech　enhancement.　The　deep　neural　networks　(DNN)　of　speech　and　noise　are　trained　separately　to　select　their　matched　codebook　entries　offline.　At　online　stage,　acoustic　features　are　firstly　extracted　from　noisy　speech　as　the　input　of　DNNs.　Then,　the　optimal　codebook　entries　of　speech　and　noise　are　selected　based　on　all　codebook　entries＇　selection　probabilities　derived　from　their　respective　DNNs.　At　last,　the　codebook-driven　Wiener　filter　is　constructed　by　these　optimal　codebook　entries　of　speech　and　noise.　Such　approach　increases　the　selection　accuracy　of　optimal　codebook　entries　comparing　with　conventional　codebook-driven　methods.　Since　the　conventional　codebook-driven　method　is　only　used　to　model　the　spectral　shape　but　not　the　spectral　details,　which　brings　much　residual　noise　between　harmonics.　In　order　to　solve　that,　the　harmonic　emphasis　technique　is　adopted　to　update　the　codebook-driven　Wiener　filter.　The　test　results　confirm　that　our　proposed　method　achieves　better　performance　compared　with　some　existing　approaches.　©　2017　IEEE.