Speech　enhancement　is　an　important　task　of　improving　speech　quality　in　noise　scenario.　Many　speech　enhancement　methods　have　achieved　remarkable　success　based　on　the　paired　data.　However,　for　many　tasks,　the　paired　training　data　is　not　available.　In　this　paper,　we　present　a　speech　enhancement　method　for　the　unpaired　data　based　on　cycle-consistent　generative　adversarial　network　(CycleGAN)　that　can　minimize　the　reconstruction　loss　as　much　as　possible.　The　proposed　model　employs　two　discriminators　and　two　generators　to　preserve　speech　components　and　reduce　noise　so　that　the　network　could　map　features　better　for　the　unseen　noise.　In　this　method,　the　generators　are　used　to　generate　the　enhanced　speech,　and　two　discriminators　are　employed　to　discriminate　real　inputs　and　the　outputs　of　the　generators.　The　experimental　results　showed　that　the　proposed　method　effectively　improved　the　performance　compared　to　traditional　deep　neural　network　(DNN)　and　the　recent　GAN-based　speech　enhancement　methods.　©　2019　IEEE.