Fantastic 4 system for NIST 2015 Language Recognition Evaluation
2016
This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), 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).
Keywords:
- Artificial intelligence
- Machine learning
- Logistic regression
- Natural language processing
- Computer science
- NIST
- Speech recognition
- Support vector machine
- Language identification
- Artificial neural network
- Probabilistic logic
- Linear discriminant analysis
- language recognition
- probabilistic linear discriminant analysis
- deep neural networks
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