NTU System at MediaEval 2015: Zero Resource Query by Example Spoken Term Detection Using Deep and Recurrent Neural Networks
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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| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Keyword Spotting | QUESST | NTU rnn (eval) | Cnxe | 2.0067 | # 60 | |
| Keyword Spotting | QUESST | NTU dtw (eval) | Cnxe | 2.0067 | # 60 | |
| Keyword Spotting | QUESST | NTU rnn (dev) | Cnxe | 2.0066 | # 58 | |
| Keyword Spotting | QUESST | NTU dtw (dev) | Cnxe | 2.0066 | # 58 |
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