Speech endpoint detection is one key technology for speech recognition. The paper proposed two kinds of endpoint detection methods: the algorithm based on the wavelet coefficient variance and the algorithm based on the sub-band average amplitude variance. Speech signal with noise was decomposed by wavelet to investigate the statistic characteristics of wavelet coefficient and sub-band amplitude. Their variances were extracted as feature to make endpoint detection. The first method?s adaptability is better than the second method, but its complexity is higher than the second method. So the synthesized speech end-point detection algorithm that is consisted of above two methods was proposed. It can select a suitable way to make operation according to noise type. Thus it can increase system efficiency and implement endpoint detection. Simulations were made under different signal-to-noise ratios and the results show that this method is efficient to segment noisy speech even at a low signal-to-noise ratio.