This　paper　presents　a　method　for　direction　of　arrival　(DOA)　estimation　of　multiple　speech　sources　based　on　the　temporal　correlation　and　local-frequency　stationarity　of　speech　signals.　The　distribution　analysis　of　single-source　points　(SSPs)　in　a　recorded　signal　shows　that　in　the　time-frequency　(T-F)　domain,　the　SSPs　are　distributed　in　the　form　of　a　small　cluster.　According　to　this　distribution,　a　method　for　DOA　estimation　of　multiple　sound　sources　is　developed　based　on　the　continuity　between　adjacent　T-F　points.　In　addition,　low-reverberation　single-source　(LRSS)　points　are　detected　based　on　the　phase　consistency　and　used　as　guidance　to　detect　whether　adjacent　T-F　points　are　SSPs.　The　direction　deviations　between　adjacent　frequency　points　and　between　adjacent　frames　are　used　as　the　SSP　detection　criteria　considering　the　temporal　correlation　and　local-frequency　stationarity.　The　kernel　density　estimation　and　peak　search　are　performed　to　obtain　the　dynamic　DOA　estimation　range　of　each　source.　Finally,　DOA　estimates　of　each　source　are　obtained　by　statistical　weighting-based　fine　localization.　Experiments　under　both　simulated　and　real　conditions　show　that　the　proposed　method　can　achieve　better　localization　performance　than　several　existing　methods.　(c)　2022　Elsevier　Ltd.　All　rights　reserved.