Paper Info
Title
The NNI Query-by-Example System for MediaEval 2015.
Abstract
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.
Year
Venue
Field
2015
MediaEval
Cross entropy,Bottleneck,Noisy data,Normalization (statistics),Speech recognition,Robustness (computer science),Query by Example,Engineering
DocType
Citations 
PageRank 
Conference
5
0.38
References 
Authors
7
20
Name
Order
Citations
PageRank
Jingyong Hou172.47
Van Tung Pham2408.42
Cheung-Chi Leung324425.37
Lei Wang4242.74
Haihua Xu55511.41
Hang Lv6101.50
Lei Xie742564.87
Zhong-Hua Fu8529.96
Chongjia Ni9101.13
Xiong Xiao1028134.97
Hongjie Chen11406.31
Shaofei Zhang1272.17
Sining Sun13365.94
Yougen Yuan1482.78
Pengcheng Li1571.43
Tin Lay Nwe1647934.59
Sunil Sivadas17312.19
Bin Ma18180.93
Eng Siong Chng19970106.33
Haizhou Li203678334.61