In　this　paper,　we　propose　a　speech　enhancement　scheme　by　only　using　a　priori　codebook　of　speech　spectral　shapes　based　on　a　novel　spectral　distortion　measure.　The　noise　signal　is　estimated　by　Minima　Controlled　Recursive　Averaging　(MCRA)　algorithm　instead　of　using　noise　spectral　shapes　stored　in　trained　codebooks.　Then,　combining　with　the　cross-correlation　between　the　spectra　of　noisy　speech　and　noise,　a　weighting　spectral　distortion　measure　is　proposed　for　optimizing　the　spectral　shapes　and　the　spectral　gains　of　speech　and　noise.　In　order　to　ensure　the　smooth　of　speech　spectrum,　the　line　spectrum　frequencies　(LSF)　that　describe　speech　spectral　shapes　are　interpolated　linearly.　While　a　priori　signal　to　noise　ratio　(SNR)　information　is　also　contributed　to　further　suppress　the　noise　energy.　Finally,　the　noisy　speech　is　passed　through　the　reconstructed　Wiener　filter　to　obtain　the　enhanced　speech.　The　objective　tests　show　that　the　proposed　method　has　better　performance　in　restraining　the　fluctuated　background　noise　than　the　conventional　codebook-based　method　to　a　large　extent.