The particle filter (PF) algorithm is appropriate to solve the problem of speaker tracking in a reverberant and noisy environment using distributed pairwise microphone networks. First, complete the tracking task based on PF algorithm in centralized manner, a processing center is required to collect the signal from all microphones to carry out the PF processing. The computation complexity and time consumption of the particle filter algorithm are relatively high, mainly because of the large number of particles exploited in the filtering process since the effectiveness and accuracy of the particle filter particularly rely on the sample set size. However, almost all the existing particle filtering algorithms exploit the fixed number of particles, especially in the field of acoustic source tracking. To deal with this matter, Kullback-Leibler distance (KLD) sampling method was utilized as an adaptation technique to adjust the sample size instead of setting fixed number. Two approaches based on particle filter algorithm for tracking speaker in distributed way are proposed. Compared to the centralized scheme, each microphone pair in the distributed network executes the local PF individually and exchanges local weights or posterior parameters among neighboring nodes to efficiently achieve the global estimate of the sound source position. Finally, simulation experiments demonstrate these two methods are feasible to track the speaker in distributed microphone networks with a variable number of particles.