This　paper　presents　a　novel　technique　for　estimating　autoregressive　(AR)　parameters　of　speech　and　noise　in　the　codebook-driven　Wiener　filtering　speech　enhancement　method.　We　only　train　the　shape　codebook　of　speech　spectrum　offline,　and　the　shape　of　noise　spectrum　is　estimated　online　for　solving　the　problem　of　noise　classification.　Unlike　conventional　codebook-driven　methods,　we　exploit　a　multiplicative　update　rule　to　estimate　the　AR　gains　of　speech　and　noise　more　accurately.　Meanwhile,　the　Bayesian　parameter-estimator　without　the　noise　codebook　is　also　developed.　Moreover,　we　achieve　the　goal　of　removing　the　residual　noise　between　the　harmonics　of　noisy　speech　by　utilizing　a　very　simple　method,　i.e.,　combining　the　codebook-driven　Wiener　filter　with　the　speech-presence　probability　(SPP).　The　test　results　confirm　the　superiority　of　our　method.　©　2016　IEEE.