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This paper studies the overlapped speech detection for improving the performance of the summed channel speaker recognition system in NIST Speaker Recognition Evaluation (SRE). The speaker recognition system includes four main modules: voice activity detection, speaker diarization, overlapped speaker detection and speaker recognition. We adopt a GMM based overlapped speaker detection system, by using entropy, MFCC and LPC features, to remove the overlapped segments in summed channel test condition. With the overlapped speech detection, the speaker diarization achieves a relative 18% diarization error rate reduction for the 2008 NIST SRE summed channel test set, and we obtain relative equal error rate reductions of 13.3% and 9.4% in speaker recognition on the 1conv-summed task and 8conv-summed task, respectively.