Paper Info
Title
Fantastic 4 system for NIST 2015 Language Recognition Evaluation.
Abstract
This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), the Laboratoire du0027Informatique de lu0027Universitu0027e du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE). The submitted system is a fusion of nine sub-systems based on i-vectors extracted from different types of features. Given the i-vectors, several classifiers are adopted for the language detection task including support vector machines (SVM), multi-class logistic regression (MCLR), Probabilistic Linear Discriminant Analysis (PLDA) and Deep Neural Networks (DNN).
Year
Venue
Field
2016
arXiv: Computation and Language
Probabilistic linear discriminant analysis,Computer science,Support vector machine,Speech recognition,NIST,Language recognition,Artificial intelligence,Natural language processing,Language identification,Logistic regression,Machine learning,Deep neural networks
DocType
Volume
Citations 
Journal
abs/1602.01929
1
PageRank 
References 
Authors
0.35
12
17
Name
Order
Citations
PageRank
Kong-Aik Lee170960.64
Ville Hautamäki238533.51
Anthony Larcher336224.91
Wei Rao4678.73
Hanwu Sun59814.15
Trung Hieu Nguyen6447.08
Guangsen Wang7284.98
Aleksandr Sizov8964.54
Ivan Kukanov932.40
Amir Poorjam1071.82
Trung Ngo Trong1122.40
Xiong Xiao1228134.97
Chenglin Xu13208.30
haihua xu14262.72
Bin Ma1533728.61
Haizhou Li163678334.61
Sylvain Meignier1765049.58