Speaker　recognition　is　a　cutting-edge　technology　that　focuses　on　identifying　individuals　based　on　their　unique　voice　characteristics.　To　address　the　challenges　associated　with　data　collection,　we　have　leveraged　deep　learning　techniques　to　introduce　two　innovative　and　lightweight　speaker　recognition　models:　Sinc-MN1D　and　AAM-Sinc-MN1D.　These　models　integrate　the　latest　advancements　in　deep　learning　and　speaker　verification　by　utilizing　a　modified　MobileNetV2　framework　as　the　core　module.To　capture　essential　short-term　speaker　features　effectively,　we　have　meticulously　replaced　the　initial　convolutional　layer　of　the　backbone　network　with　a　positively　modified　convolutional　layer　inspired　by　the　optimized　SincNet.　Furthermore,　to　enhance　the　extraction　of　critical　frequency　features,　we　have　incorporated　the　AAM-softmax　loss　function,　commonly　used　in　face　recognition,　to　enhance　the　models　capability　in　identifying　challenging　samples.　Our　method　has　been　rigorously　evaluated　on　the　TIMIT　dataset,　demonstrating　superior　performance　compared　to　the　baseline　approach.