In　many　speech　separation　methods,　the　contextual　information　contained　in　the　feature　sequence　is　mainly　modeled　by　recurrent　layer　and/or　self-attention　mechanism.　However,　how　to　combine　these　two　powerful　approaches　more　effectively　needs　to　be　explored.　In　this　paper,　a　recurrent　attention　with　parallel　branches　is　proposed　to　first　fully　exploit　the　contextual　information　contained　in　the　time-frequency　(T-F)　features.　Then,　this　information　is　further　modeled　by　the　recurrent　modules　in　a　conventional　manner.　Specifically,　the　proposed　recurrent　attention　with　parallel　branches　uses　two　attention　modules　stacked　sequentially.　Each　attention　module　has　two　parallel　branches　of　self-attention　to　model　dependencies　along　two　axes　and　one　convolutional　layer　for　feature　fusion.　Thus,　the　contextual　information　contained　in　the　T-F　features　can　be　fully　exploited　and　further　modeled　by　the　recurrent　modules.　Experimental　results　showed　the　effectiveness　of　our　proposed　method.　©　2023　International　Speech　Communication　Association.　All　rights　reserved.