Many　speech　enhancement　algorithms　that　deal　with　noise　reduction　are　based　on　a　binary　masking　decision　(termed　as　the　hard　decision),　which　may　cause　some　regions　of　the　synthesized　speech　to　be　discarded.　In　view　of　the　problem,　a　soft　decision　is　often　used　as　an　optimal　technique　for　speech　restoration.　In　this　paper,　considering　a　new　fashion　of　speech　and　noise　models,　we　present　two　model-based　soft　decision　techniques.　One　technique　estimates　a　ratio　mask　generated　by　the　exact　Bayesian　estimators　of　speech　and　noise.　For　the　second　technique,　we　consider　one　issue　that　an　optimum　local　criterion　(LC)　for　a　certain　SNR　may　not　be　appropriate　for　other　SNRs.　So　we　estimate　a　probabilistic　mask　with　a　variable　LC.　Experimental　results　show　that　the　proposed　method　achieves　a　better　performance　than　reference　methods　in　speech　quality.