Speech　enhancement　is　a　critical　part　of　variety　types　of　communication　systems　and　automatic　speech　recognition　(ASR)　applications.　In　this　study　we　propose　a　speech　enhancement　method　for　real　time　VoIP　applications　with　stacked　frames　and　deep　neural　network,　a　novel　data　preparation　approach　is　also　introduced.　In　contrast　to　many　states　of　art　learning-based　method,　we　focused　on　real-time　implement　in　VoIP　applications.　Experiments　were　conducted　on　speech　degraded　by　different　noise　types　and　SNR　levels　which　were　not　seen　in　the　training　stage　of　the　deep　neural　network　and　achieved　a　significant　improvement　on　PESQ.　Important　traditional　real-time　speech　enhancement　method　and　most　recent　states　of　art　learning-based　method　were　also　tested　and　compared　with　proposed　method.　The　results　show　that　proposed　method　effectively　improve　the　speech　intelligibility,　greatly　outperform　traditional　real-time　minimum-mean　square　error　(MMSE)　algorithm　and　real-time　learning-based　CNN　method　in　PESQ.　We　also　achieve　comparable　PESQ　in　comparison　with　most　recent　state　of　the　art　learning-based　method,　but　outperform　it　in　time　complexity.　Making　this　method　attractive　in　VoIP　communication　system　applications　which　is　high　demand　on　communication　latency.　©　2019　SPIE.