In　this　paper,　we　present　a　novel　method　for　estimating　short-term　linear　predictive　parameters　of　speech　and　noise　in　the　codebook-drivenWiener　filtering　speech　enhancement　method.　We　only　use　pretrained　spectral　shape　codebook　of　speech　to　model　the　a　priori　information　about　linear　predictive　coefficients　of　speech,　and　the　spectral　shape　of　noise　is　estimated　online　directly　instead　of　using　noise　codebook　to　solve　the　problem　of　noise　classification.　Differing　from　the　existing　codebook-driven　methods　that　the　linear　predictive　gains　of　speech　and　noise　are　estimated　by　maximum-likelihood　method,　in　the　proposed　method　we　exploit　a　multiplicative　update　rule　to　estimate　the　linear　predictive　gains　more　accurately.　The　estimated　gains　can　help　to　reserve　more　speech　components　in　the　enhanced　speech.　Meanwhile,　the　Bayesian　parameter-estimator　without　the　noise　codebook　is　also　developed.　Moreover,　we　develop　an　improved　codebook-driven　Wiener　filter　combined　with　the　speech-presence　probability,　so　that　the　proposed　method　achieves　the　goal　of　removing　the　residual　noise　between　the　harmonics　of　noisy　speech.