In　this　paper,　we　propose　a　codebook-based　Bayesian　linear　predictive　(LP)　parameters　estimation　for　speech　enhancement,　in　which　the　LP　parameters　are　estimated　based　on　the　current　and　past　frames　of　noisy　speech.　First,　by　using　hidden　Markov　model　(HMM),　we　develop　a　new　method　to　drive　the　speech　presence　probability　(SPP)　and　speech　absence　probability　(SAP).　These　two　probabilities　are　the　weighting　coefficients　for　the　estimated　LP　parameters　corresponding　to　speech　presence　and　speech　absence　states.　Then　we　exploit　the　normalized　cross-correction　to　adjust　the　transition　probabilities　between　speech-presence　and　speech-absence　states　of　HMM.　The　proposed　adjustment　method　makes　the　SPP　estimation　more　accurately.　Finally,　in　order　to　suppress　the　noise　between　the　harmonics　of　voiced　speech,　we　employ　the　a　posteriori　SPP　to　modify　the　Wiener　filter　for　enhancing　the　noisy　speech.　Our　experiments　demonstrate　that　the　proposed　method　is　superior　to　the　reference　methods.