Overlapping　speech　is　one　of　the　main　factors　influencing　the　performance　of　speaker　segmentation.　This　paper　presents　an　overlapping　speech　detection　method　using　a　high-level　information　feature　to　improve　the　speaker　segmentation　results.　A　linguistic　high-level　information　feature　of　the　speech　is　extracted　using　the　universal　background　model　(UBM).　Then,　a　hidden　Markov　model　(HMM)　is　trained　using　the　Mel　frequency　cepstral　coefficients　(MFCC)　and　the　high-level　information　to　detect　overlapping　speech.　The　result　is　then　used　for　the　speaker　segmentation　of　the　pre-processed　speech.　Tests　on　a　dataset　generated　from　the　TIMIT　database　show　that　the　error　ratio　for　overlapping　speech　detection　is　significantly　lower　than　the　reference　method　using　just　the　MFCC　feature.　The　speaker　segmentation　is　also　significantly　improved.　©　2017,　Tsinghua　University　Press.　All　right　reserved.