This paper presents a method of query-by-example spoken term detection(QbE STD) using segmental dynamic time warping(SDTW) and lower-bound estimate(LBE). The approach is designed for low-resource situations in which limited or no in-domain training material is available. According to this method, the phone posterior probabilities of query examples and test materials should first of all be got, and then the candidate segments are selected in test materials and the lower-bound estimates of actual DTW scores are computed between the query example and all candidate segments in test materials quickly. the K nearest neighbor (KNN) search algorithm is chosen to search for the segments that have maximal similarity. Finally, the retrieval results can be modified by pseudo relevance feedback(PRF). The experimental result indicates that although there is a slightly degraded in retrieval precision when compared with formulating a DTW procedure directly, the retrieval speed of the method presented by this paper has a big advantage over the latter, and the retrieval precision can be enhanced availably after the retrieval results modified by PRF. .