In　this　paper,　we　propose　a　speech　and　noise　auto-regressive　(AR)　model　parameters　estimation　method　under　noisy　conditions　used　for　speech　enhancement,　which　exploits　a　priori　information　about　speech　and　noise　spectral　shapes　(parameterized　as　AR　coefficients)　described　by　trained　codebooks.　The　expectation　maximization　(EM)　algorithm　is　first　employed　to　obtain　AR　gains　of　speech　and　noise,　which　correspond　to　each　pair　of　codebook　entries　of　speech　and　noise　spectral　shapes.　Then　the　K-nearest　neighbor　(KNN)　rule　is　used　to　select　some　candidates　from　the　optimized　AR　parameters　(AR　coefficients　and　AR　gains)　of　speech　and　noise　for　constructing　the　weighted　Wiener　filter　(WWF).　Furthermore,　by　using　sigmoid　function,　we　propose　a　posteriori　speech-presence　probability　(SPP)　estimation　method.　Combining　the　a　posteriori　SPP　with　the　WWF,　the　residual　noise　of　enhanced　speech　is　effectively　reduced.　The　test　results　demonstrate　the　performance　superiority　of　the　proposed　speech　enhancement　scheme　compared　to　the　reference　methods.　(c)　2016　Elsevier　B.V.　All　rights　reserved.