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This work is a part of our research on scalable and/or distributed fusion and sensor calibration. We address parameter estimation in multi-sensor state space models which underpins surveillance applications with sensor networks. The parameter likelihood of the problem involves centralised Bayesian filtering of multi-sensor data, which lacks scalability with the number of sensors and induces a large communication load. We propose separable likelihoods which approximate the centralised likelihood with single sensor filtering terms.
In this work, we propose a novel framework for rescoring keyword search (KWS) detections using acoustic samples extracted from the training data. We view the keyword rescoring task as an information retrieval task and adopt the idea of query expansion. We expand a textual keyword with multiple speech keyword samples extracted from the training data. In this way, the hypothesized detections are compared with the multiple keywords using non-parametric approaches such as dynamic time warping (DTW).