In noisy and reverberant environments, the problem of acoustic source localisation and tracking (ASLT) using an array of microphones presents a number of challenging difficulties. One of the main issues when considering real-world situations involving human speakers is the temporally discontinuous nature of speech signals: the presence of silence gaps in the speech can easily misguide the tracking algorithm, even in practical environments with low to moderate noise and reverberation levels. A natural extension of currently available sound source tracking algorithms is the integration of a voice activity detection (VAD) scheme. We describe a new ASLT algorithm based on a particle filtering (PF) approach, where VAD measurements are fused within the statistical framework of the PF implementation. Tracking accuracy results for the proposed method is presented on the basis of synthetic audio samples generated with the image method, whereas performance results obtained with a real-time implementation of the algorithm, and using real audio data recorded in a reverberant room, are published elsewhere. Compared to a previously proposed PF algorithm, the experimental results demonstrate the improved robustness of the method described in this work when tracking sources emitting real-world speech signals, which typically involve significant silence gaps between utterances.