Variations　of　speech　content　increase　the　difficulty　of　speaker　verification.　In　this　paper,　to　alleviate　the　negative　effect　of　the　variations,　phoneme-unit-specific　time-delay　neural　network　(PUSTDNN)　is　proposed　and　applied　to　the　state-of-the-art　x-vector　system.　It　models　each　phoneme　unit　with　an　individual　time-delay　neural　network　(TDNN).　That　is　to　say,　each　TDNN　mainly　deals　with　a　phoneme　unit.　Compared　with　handling　all　phoneme　units　together,　when　handling　a　phoneme　unit,　a　TDNN　can　extract　more　discriminative　speaker　information,　thus　improving　the　system　performance.　Two　realizations　of　the　PUSTDNN　are　proposed.　The　first　one　can　retain　speech　temporal　information.　The　second　one　further　combines　all　the　TDNNs　in　a　PUSTDNN　into　a　larger　TDNN　to　reduce　computational　complexity.　To　avoid　model　overfitting,　the　phoneme　units　are　obtained　by　clustering　phonemes　based　on　the　phonetic　knowledge　and　phonetic　sparsity　degree.　The　PUSTDNN　is　also　compared　with　two　other　techniques,　i.e.,　phonetic　vector　and　multitask.　Experiments　on　the　Fisher,　NIST　SRE10,　and　VoxCeleb　datasets　show　that　the　phonetic　vector　technique　is　most　robust　to　the　phoneme　unit　recognition　accuracy.　When　the　accuracy　is　high　enough,　the　multitask　performs　better　than　the　phonetic　vector,　and　the　PUSTDNN　performs　best　and　can　achieve　over　10%　relative　improvement　compared　with　the　x-vector　baseline.　©　2014　IEEE.