A Stage Match for Query-by-Example Spoken Term Detection Based On Structure Information of Query

The state-of-the-art of query-by-example spoken term detection (QbE-STD) strategies are usually based on segmental dynamic time warping (S-DTW). However, the sliding window in S-DTW may separate signal of a word into different segments and produce many illegal candidates required to be compared with the query, which significantly reduce the accuracy and efficiency of detection. In this paper, we propose a stage match strategy based on the structure information of the query, represented with the unvoiced-voiced attribute of the portions in itself. The strategy first locates potential candidates with similar structure against the query in utterances, and further matches the query with Type-Location DTW (TL-DTW), which is a modified DTW with the constraints of pronunciation types and relative positions of paired frames in the voiced sub-segments. Experiments on AISHELL-1 Corpus showed that the proposed approach achieved a relative improvement of 30.51% in AUC against S-DTW and speeded up the retrieval.