Papers › The NNI Query-by-Example System for MediaEval 2015

The NNI Query-by-Example System for MediaEval 2015

14 Sep 2015MediaEval 2015 Workshop 2015 9archive 2025-07-28

Jingyong Hou, Van Tung Pham, Cheung-Chi Leung, Lei Wang, HaiHua Xu, Hang Lv, Lei Xie, Zhonghua Fu, Chongjia Ni, Xiong Xiao, Hongjie Chen, Shaofei Zhang, Sining Sun, Yougen Yuan, Pengcheng Li, Tin Lay Nwe, Sunil Sivadas, Bin Ma, Eng Siong Chng, Haizhou Li

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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Keyword Spotting

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Keyword Spotting QUESST NNI (eval) ATWV 0.270 #9 of 69 Archive leaderboard report
Keyword Spotting QUESST NNI (eval) Cnxe 0.761 #9 of 69 Archive leaderboard report
Keyword Spotting QUESST NNI (eval) MTWV 0.274 #9 of 69 Archive leaderboard report
Keyword Spotting QUESST NNI (eval) MinCnxe 0.747 #9 of 69 Archive leaderboard report
Keyword Spotting QUESST NNI (dev) ATWV 0.286 #10 of 69 Archive leaderboard report
Keyword Spotting QUESST NNI (dev) Cnxe 0.773 #10 of 69 Archive leaderboard report
Keyword Spotting QUESST NNI (dev) MTWV 0.286 #10 of 69 Archive leaderboard report
Keyword Spotting QUESST NNI (dev) MinCnxe 0.757 #10 of 69 Archive leaderboard report

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