Fantastic 4 system for NIST 2015 Language Recognition Evaluation
Kong Aik Lee, Ville Hautam\"aki, Anthony Larcher, Wei Rao, Hanwu Sun,, Trung Hieu Nguyen, Guangsen Wang, Aleksandr Sizov, Ivan Kukanov, Amir, Poorjam, Trung Ngo Trong, Xiong Xiao, Cheng-Lin Xu, Hai-Hua Xu, Bin Ma,, Haizhou Li, Sylvain Meignier

TL;DR
This paper presents a fusion-based language recognition system for the 2015 NIST LRE, combining multiple sub-systems and classifiers to improve detection accuracy.
Contribution
It introduces a multi-system fusion approach using diverse features and classifiers for language recognition in the NIST 2015 evaluation.
Findings
Fusion of nine sub-systems enhances performance.
Multiple classifiers improve language detection accuracy.
System achieves competitive results in NIST LRE 2015.
Abstract
This article describes the systems jointly submitted by Institute for Infocomm (IR), the Laboratoire d'Informatique de l'Universit\'e 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).
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Taxonomy
TopicsSpeech Recognition and Synthesis · Natural Language Processing Techniques · Topic Modeling
MethodsLogistic Regression
