For　the　neural　network-based　speech　Packet　Loss　Concealment　(PLC),　the　input　features　are　crucial　factors　that　directly　affect　the　final　recovery　performance.　Additionally,　the　challenge　of　restoring　high　natural　speech　through　PLC　remains　to　be　addressed.　To　effectively　recover　packet　loss　speech　and　improve　its　naturalness,　this　paper　proposes　a　PLC　method　of　speech　signal　based　on　the　priori　Mel-spectrum　and　neural　vocoder.　The　proposed　method　adopts　an　asymmetric　encoding　and　decoding　network　structure.　At　the　encoding　stage,　this　method　utilizes　two　independent　encoding　networks　to　extract　the　latent　time-frequency　features　from　the　waveform　and　Mel-spectrogram,　respectively.　At　the　decoding　stage,　the　latent　time-frequency　features　are　jointly　fed　into　a　neural　vocoder　which　is　composed　of　several　temporal　adaptive　denor-malization　layer　to　restore　the　lost　speech　signals　and　enhance　the　naturalness.　Simulation　experiments　demonstrate　that　the　proposed　method　outperforms　two　existing　packet　loss　concealment　algorithms　in　terms　of　perceptual　evaluation　of　speech　quality　and　short-time　objective　intelligibility.　©　2024　Chinese　Institute　of　Electronics.　All　rights　reserved.