Studies　in　speech　separation　have　achieved　significant　success　in　recent　years.　To　correctly　separate　the　mixture　signals,　it　is　critical　to　encode　the　signals　into　an　appropriate　latent　space.　Existing　speech　separation　methods　include　transforming　mixed　signals　into　frequency　domain　space　or　time　domain　space.　The　frequency　domain　features　(spectrogram)　are　generated　by　STFT,　which　is　closely　related　to　speech　articulation　and　reflects　the　energy　of　speech　directly.　The　time　domain　features　are　learned　from　a　latent　embedding　space,　and　the　separation　effect　is　facilitated　by　the　end-to-end　structure.　However,　these　methods　are　based　on　the　representations　from　only　one　domain,　which　is　insufficient　for　providing　a　speech　separation　encoding　space　that　is　completely　separable.　Therefore,　a　Triple-Path　Recurrent　Neural　Network　(TPRNN)　that　fuse　features　from　two　domains　is　proposed.　It　employs　a　spectrogram　as　auxiliary　information　to　improve　the　performance　of　speech　separation.　Experimental　results　on　the　Wall　Street　Journal　(WSJ0)　dataset　show　that　this　approach　is　beneficial　to　improve　speech　separation　performance.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.