Efficient　speaker　segmentation　and　clustering　method　based　on　the　improved　spectral　clustering　is　proposed　in　this　paper.　Traditional　speaker　segmentation　and　clustering　is　performed　by　the　hierarchical　clustering　algorithms　with　Bayesian　information　criterion　(BIC)　metric　and　cross　likelihood　ratio　(CLR)　metric　after　the　speakers　are　segmented.　Since　this　method　has　high　computational　complexity　and　may　result　in　a　suboptimal　solution,　we　use　spectral　clustering　to　overcome　this　problem　and　improve　the　performance　of　clustering　algorithm.　First　the　affinity　matrix　is　constructed　with　the　mean　supervector　feature　transformed　by　KL　kernel　mapping.　And　then　the　scaling　parameter　is　selected　adaptively.　The　experiments　performed　on　the　NIST　1998　multi-speaker　corpus　show　that　the　proposed　method　outperforms　the　baseline　system.