Recently,　deep　neural　networks　(DNN)　has　been　employed　to　conduct　a　kind　of　packet　loss　concealment　(PLC)　method　for　digital　speech　transmission.　Due　to　good　mapping　ability　of　the　DNN,　the　DNN-based　PLC　method　usually　can　achieve　better　speech　recovery　than　some　traditional　methods.　However,　because　the　phase　of　speech　is　limited　between　-pi　and　pi　and　there　is　no　obvious　spectral　structure　like　magnitude　spectrum,　it　is　not　suitable　for　the　DNN　learning.　The　DNN-based　PLC　method　is　usually　difficult　to　accurately　estimate　the　phase　of　the　lost　speech,　which　also　limits　the　quality　of　the　recovered　speech.　In　order　to　solve　the　problem　of　inaccurate　phase　estimation　on　the　DNN,　a　new　PLC　method　based　on　phase　unwrapping　is　proposed　in　this　paper.　This　method　is　divided　into　two　stages:　training　stage　and　test　stage.　In　the　training　stage,　we　first　employ　cellular　automata　(CA)　to　unwrap　the　phase　of　speech　for　making　its　spectrum　structural.　Then　the　unwrapped　phase　spectrum　is　used　as　the　input　feature　and　the　training　target　to　train　the　DNN　model.　The　input　feature　of　the　DNN　is　consisted　of　the　unwrapped　phase　spectra　of　few　frames　in　front　of　the　lost　speech,　and　the　training　target　is　the　unwrapped　phase　spectrum　of　the　lost　speech.　In　the　test　stage,　firstly,　the　unwrapped　phase　of　few　frames　in　front　of　the　lost　speech　is　extracted　as　the　input　features　of　the　DNN　to　obtain　the　unwrapped　phase　of　the　lost　speech.　Then　the　unwrapped　phase　of　the　lost　speech　is　re-wrapped　into　the　phase　of　the　lost　speech.　Finally,　combining　with　the　estimated　phase　spectrum　and　logarithmic　power　spectrum　(LPS)　of　the　lost　speech,　we　can　recover　the　lost　speech　by　the　PLC.　Experimental　results　show　that,　compared　with　the　existing　DNN-based　PLC　method,　the　proposed　PLC　method　can　better　recover　the　lost　speech　and　improve　the　quality　of　speech,　which　is　suitable　for　the　PLC　of　digital　speech　transmission.