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). The obtained similarity scores are used in a graph based method to re-rank the original confidence scores estimated by the automatic speech recognition (ASR) systems. Experimental results on the NIST OpenKWS15 Evaluation show that our rescoring method is effective, especially for the subword system. For subword experiments, the graph-based rescoring with training samples obtains 5.1% and 1.5% absolute improvement over two baseline systems. One is a standard parametric ASR system, while the other is the graph-based rescoring without training samples