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This paper presents initial developments towards computational hearing models that move beyond stationary microphone assumptions. We present a particle filtering based system for using localisation cues to track speaker changes in meeting recordings. Recording are made using in-ear binaural microphones worn by a listener whose head is constantly moving. Tracking speaker changes requires simultaneously inferring the perceiver's head orientation, as any change in relative spatial angle to a source can be caused by either the source moving or the microphones moving. In real applications, such as robotics, there may be access to external estimates of the perceiver's position. We investigate the effect of simulating varying degrees of measurement noise in an external perceiver position estimate. We show that only limited self-position knowledge is needed to greatly improve the reliability with which we can decode the acoustic localisation cues in the meeting scenario.