Joint short-time speaker recognition and tracking using sparsity-based source detection

A random finite set-based sequential Monte-Carlo tracking method is proposed to track multiple acoustic sources in indoor scenarios. The proposed method can improve tracking performance by introducing recognized speaker identities from the received signals. At the front-end, the degenerate unmixing estimation technique (DUET) is employed to separate the mixed signals, and the time delay of arrival (TDOA) is measured. In addition, a criterion to select the reliable microphone pair is designed to quickly obtain accurate speaker identities from the mixed signals, and the Gaussian mixture model universal background model (GMM-UBM) is employed to train the speaker model. In the tracking step, the update of the weight for each particle is derived after introducing the recognized speaker identities, which results in better association between the measurements and sources. Simulation results demonstrate that the proposed method can improve the accuracy of the filter states and discriminate the sources close to each other.