A　Gaussian　Mixture　Model　(GMM)　based　speech　enhancement　method　in　compressed　domain　used　for　ITU-T　G.　722.2　wideband　speech　codec　is　proposed　to　take　full　advantage　of　the　prior　knowledge　of　the　Immittance　Spectral　Frequencies　(ISFs)　for　the　clean　speech.　Firstly,　GMM　is　adopted　to　model　the　joint　probability　density　of　feature　vectors　which　are　composed　by　the　ISFs　of　noisy　speech　and　clean　speech　with　the　corresponding　gain　scaling　factor.　Secondly,　an　optimal　Bayesian　estimation　of　feature　parameters　derived　from　clean　speech　is　obtained　under　the　minimum　mean　square　error　(MMSE)　criterion.　To　be　compatible　with　the　DTX　(Discontinuous　Transmission)　mode,　the　logarithmic　energy　is　attenuated　and　the　ISFs　remain　when　a　SID　(Silence　Insertion　Descriptor)　frame　is　received.　Furthermore,　if　ao　erased　frame　is　received,　the　bit　stream　is　unchanged　and　the　proposed　method　is　performed　on　the　recovered　parameters　for　the　memory　update.　The　evaluation　is　conducted　under　the　ITU-T　G.　160.　The　results　indicate　that,　comparing　with　the　reference　method,　the　proposed　method　can　produce　larger　amount　of　noise　level　reduction　with　better　objective　speech　quality,　while　the　SNR　improvement　remains　acceptable.