In　this　paper,　we　propose　a　novel　speech　enhancement　method　by　using　power　spectra　smooth　of　the　speech　and　noise　in　Wiener　filtering　based　on　the　fact　that　a　priori　SNR　in　standard　Wiener　filtering　reflects　the　power　ratio　of　speech　and　noise　in　frequency　bins.　This　power　ratio　also　could　be　approximated　by　the　smoothed　spectra　of　speech　and　noise.　We　estimate　the　power　spectra　of　noise　and　speech　by　means　of　minima　controlled　recursive　averaging　method　and　spectral-subtractive　principle,　respectively.　Then,　the　linear　prediction　analysis　is　used　to　smooth　power　spectra　of　the　speech　and　noise　in　frequency　domain.　Finally,　we　utilize　cross　correlation　between　the　power　spectra　of　the　noisy　speech　and　noise　to　modify　gains　of　the　power　spectra　for　further　reducing　noise　in　silence　and　unvoiced　segments.　The　objective　test　results　show　that　the　performance　of　the　proposed　method　outperforms　conventional　Wiener　Filtering　and　Codebook-based　methods.