Aiming　at　the　problem　of　insufficient　feature　learning　of　time-delay　neural　networks,　which　is　widely　used　in　the　field　of　language　identification,　a　new　architecture　called　multi-scale　and　multi-dimensional　convolution　is　proposed.　The　structure　includes　a　global　inter-frame　correlation　network,　local　and　global　multi-scale　network,　global　channel　correlation　network,　and　multi-head　attention　statistics　pooling　layer.　The　global　inter-frame　correlation　network　models　the　global　context　at　the　initial　frame　layer　to　obtain　the　dependency　characteristics　of　the　global　context,　which　makes　up　for　the　natural　deficiency　of　time-delay　neural　network　based　on　limited　context;　local　and　global　multi-scale　networks　aggregate　the　information　within　and　between　layers　to　extract　features　on　a　finer　and　more　complex　scale;　the　global　channel　correlation　network　is　explicitly　modeled　from　the　channel　dimension　to　realize　the　adaptive　correction　of　the　channel　dimension　characteristics;　The　attention　statistics　pool　layer　is　extended　to　multiple　heads　so　that　features　can　be　distinguished　from　multiple　aspects.　Through　the　training　of　the　AP17-OLR　data　set,　it　has　been　improved　by　41%　compared　with　the　previous　excellent　model.　©　2022　SPIE.