For　deep　neural　network　(DNN)-based　methods,　the　time-frequency　(T-F)　masks　are　commonly　used　as　the　training　target.　However,　most　of　them　do　not　focus　on　the　phase　information,　while　recent　studies　have　revealed　that　incorporating　phase　information　into　the　T-F　mask　can　effectively　help　improve　the　speech　quality　of　the　enhanced　speech.　In　this　paper,　we　present　two　techniques　to　obtain　the　T-F　mask　considering　phase　information.　In　the　first　technique,　the　characteristics　about　spectral　structures　of　two　phase　differences,　which　include　the　phase　difference　(PD)　between　clean　and　noisy　speech　and　the　PD　between　noise　and　noisy　speech,　are　firstly　discussed.　Then,　considering　the　specific　characteristics　of　two　PDs,　a　parametric　ideal　ratio　mask　(IRM)　whose　parameters　are　controlled　by　the　cosines　of　the　two　aforementioned　PDs　is　proposed,　which　is　termed　as　a　bounded　IRM　with　phase　constraint　(BIRMP).　In　the　second　technique,　an　optimal　estimator　based　on　generalized　maximum　a　posteriori　(GMAP)　probability　of　complex　speech　spectrum　is　proposed　and　defined　as　an　optimal　GMAP　estimation　of　complex　spectrum　(OGMAPC).　The　OGMAPC　estimator　can　dynamically　adjust　the　scale　of　prior　information　of　spectral　magnitude　and　phase.　Considering　the　difficult　predictability　of　speech　phase　in　the　DNN-based　method,　the　second　technique　exploits　the　spectral　magnitude　part　of　the　OGMAPC　estimator　to　calculate　an　optimal　magnitude　mask　with　the　phase　information　and　its　ideal　value　is　used　for　DNN　training.　The　experiments　show　that　the　proposed　methods　can　outperform　the　reference　methods.　(C)　2019　Elsevier　Ltd.　All　rights　reserved.