Because　the　existing　single　channel　speech　enhancement　technologies　perform　not　well　in　the　tracking　and　suppression　of　non-stationary　noise,　the　speech　enhancement　method　based　on　online　energy　adjustment　is　proposed.　The　normalized　critical　band　energy　parameters　are　employed　as　the　feature　in　Gaussian　mixture　model　(GMM)　to　distinguish　the　background　noises.　Based　on　the　AR-HMM　of　clean　speech　and　the　noise　of　corresponding　type,　the　power　spectrums　of　speech　and　noise　are　estimated　under　minimum　mean　square　error　(MMSE)　criteria.　When　the　differences　between　the　training　data　and　test　data　are　considered　in　the　non-stationary　noise　environment,　the　online　adjustment　method　for　the　speech　and　noise　models　is　necessary.　The　scaling　factor　of　speech　energy　is　estimated　with　the　iterative　expectation　maximization　(EM)　algorithm　and　the　one　of　noise　energy　is　estimated　with　the　re-estimation　approach　similar　to　the　training　stage.　And　the　initial　scaling　factor　of　noise　energy　is　obtained　by　minima-controlled　recursive　averaging　(MCRA)　algorithm.　The　evaluation　of　the　proposed　method　is　performed　under　the　standard　of　ITU-T　G.160.　The　test　results　reveal　that,　comparing　with　the　two　reference　methods,　the　proposed　method　performs　well　in　non-stationary　noise　environments,　including　larger　noise　reduction　and　shorter　convergence　time.　©,　2014,　Tien　Tzu　Hsueh　Pao/Acta　Electronica　Sinica.　All　right　reserved.