To　enhance　the　perceptual　quality　of　speech　signal,　the　Packet　Loss　Concealment　(PLC)　technique　focuses　on　recovering　the　lost　speech　caused　by　network　latency　and　jitter.　In　practical　applications,　the　PLC　methods　typically　employ　a　predictive　process　that　relies　on　previously　received　speech　signal　to　recover　the　lost　speech　without　introducing　additional　delay.　In　this　paper,　we　propose　a　predictive　PLC　network,　which　employs　the　Conformer　and　temporal　convolution　module　to　fully　exploit　the　contextual　dependencies　and　to　better　predict　the　lost　speech.　In　addition,　our　proposed　network　can　be　directly　employed　as　the　generator　and　combined　with　appropriate　discriminative　networks,　forming　a　Generative　Adversarial　Network　(GAN)　paradigm　that　can　enhance　the　perceptual　quality　of　the　recovered　speech　signal.　Experimental　results　demonstrate　that　without　any　discriminative　network,　the　proposed　method　demonstrates　impressive　results　in　PLC.　Under　the　GAN　paradigm,　further　improvement　can　be　observed　and　our　proposed　method　outperforms　several　baseline　methods　at　different　packet　loss　rates.　©　2023　IEEE.