Papers › NTU System at MediaEval 2015: Zero Resource Query by Example Spoken Term Detection...
NTU System at MediaEval 2015: Zero Resource Query by Example Spoken Term Detection Using Deep and Recurrent Neural Networks
Cheng-Tao Chung, Yang-De Chen
This note serves as a documentation describing the methods the authors of this paper implemented for the Query by Example Search on Speech Task (QUESST) as a part of MediaEval 2015. In this work, we combined DTW, DNN and RNN in one framework to perform query by example spoken term detection in a zero resource setting.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Keyword Spotting | QUESST | NTU dtw (dev) | Cnxe | 2.0066 | #58 of 69 | Archive leaderboard | report |
| Keyword Spotting | QUESST | NTU rnn (dev) | Cnxe | 2.0066 | #59 of 69 | Archive leaderboard | report |
| Keyword Spotting | QUESST | NTU dtw (eval) | Cnxe | 2.0067 | #60 of 69 | Archive leaderboard | report |
| Keyword Spotting | QUESST | NTU rnn (eval) | Cnxe | 2.0067 | #61 of 69 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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