The NNI Query-by-Example System for MediaEval 2015
This paper describes the system developed by the NNI team for the Query-by-Example Search on Speech Task (QUESST) in the MediaEval 2015 evaluation. Our submitted system mainly used bottleneck features/stacked bottleneck features (BNF/SBNF) trained from various resources. We investigated noise robustness techniques to deal with the noisy data of this year. The submitted system obtained the actual normalized cross entropy (actCnxe) of 0.761 and the actual Term Weighted Value (actTWV) of 0.270 on all types of queries of the evaluation data.
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| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Keyword Spotting | QUESST | NNI (eval) | Cnxe | 0.761 | # 9 | |
| MinCnxe | 0.747 | # 14 | ||||
| ATWV | 0.270 | # 9 | ||||
| MTWV | 0.274 | # 10 | ||||
| Keyword Spotting | QUESST | NNI (dev) | Cnxe | 0.773 | # 10 | |
| MinCnxe | 0.757 | # 15 | ||||
| ATWV | 0.286 | # 8 | ||||
| MTWV | 0.286 | # 9 |
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