In　this　paper,　we　propose　a　neural　speech　coding　method　based　on　the　dual-path　conformer,　which　mainly　consists　of　three　steps:　(1)　the　encoding　and　decoding　of　the　time-frequency　spectrum　are　performed　by　a　structure　that　combines　the　CNN　and　the　dual-path　conformer,　(2)　residual　vector　quantization　is　employed　to　quantize　the　output　features　of　encoder　and　form　a　compact　discrete　representation,　and　(3)　multi-period　and　multi-scale　discriminators　are　used　to　improve　the　perceptual　quality　of　speech　during　adversarial　training.　Experimental　results,　from　both　subjective　and　objective　evaluations,　demonstrate　that　the　proposed　codec　outperforms　the　state-of-the-art　neural　codec　AudioDEC　and　the　leading　conventional　codec　Opus　in　terms　of　performance.　©2024　IEEE.