Query-by-example spoken term detection using bessel features

Vasudev, Drisya; Gangashetty, Suryakanth V.; Babu, K. K. Anish; Riyas, K S

doi:10.1109/spices.2015.7091361

Cited by 6 publications

(1 citation statement)

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“…Spoken term detection (STD) which is also known as keyword spotting (KWS), refers to detecting whether test utterances contain portions similar to the given query. According to the form of query, it can be divided into text-based STD and audio-based STD [1]. The former generally requires an automatic speech recognition (ASR) system or an end-to-end system [2,3] as the basis.…”

Section: Introductionmentioning

confidence: 99%

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

Zhan

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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.

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Section: Introductionmentioning

confidence: 99%