Distributed IMM-Unscented Kalman Filter for Speaker Tracking in Microphone Array Networks

In this paper, we first propose a distributed unscented Kalman filter (DUKF) to overcome the nonlinearity of measurement model in speaker tracking. Next, for the different motion dynamics of a speaker in the in-door environment, we introduce the interacting multiple model (IMM) algorithm and propose a distributed interacting multiple model-unscented Kalman filter (IMM-UKF) for estimating time-varying speaker's positions in a microphone array network. In the distributed IMM-UKF based speaker tracking method, the time difference of arrival (TDOA) of the speech signals received by a pair of microphones at each node is estimated by the generalized cross-correlation (GCC) method, then the distributed IMM-UKF is used to track a speaker whose position and speed significantly vary over time in a microphone array network. The proposed method can estimate speaker's positions globally in the network and obtain a smoothed trajectory of the speaker's movement robustly in noisy and reverberant environments, and it is scalable for speaker tracking. Simulation and real-world experiment results reveal the effectiveness of the proposed speaker tracking method.