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It is a great challenge to perform a sound source localization system for mobile robots, because noise and reverberation in room pose a severe threat for continuous localization. This paper presents a novel approach named guided spectro-temporal (ST) position localization for mobile robots. Firstly, since generalized cross-correlation (GCC) function based on time delay of arrival (TDOA) can not get accurate peak, a new weighting function of GCC named PHAT-ργ is proposed to weaken the effect of noise while avoiding intense computational complexity. Secondly, a rough location of sound source is obtained by PHAT-ργ method and room reverberation is estimated using such location as priori knowledge. Thirdly, ST position weighting functions are used for each cell in voice segment and all correlation functions from all cells are integrated to obtain a more optimistical location of sound source. Also, this paper presents a fast, continuous localization method for mobile robots to determine the locations of a number of sources in real-time. Experiments are performed with four microphones on a mobile robot. 2736 sets of data are collected for testing and more than 2500 sets of data are used to obtain accurate results of localization. Even if the noise and reverberation are serious. The proportion data is 92% with angle error less than 15 degrees. What's more, it takes less than 0.4 seconds to locate the position of sound source for each data.