  This work explores the use of a monolingual Deep Neural Network (DNN) model
as an universal background model (UBM) to address the problem of Language
Recognition (LR) in I-vector framework. A Time Delay Deep Neural Network
(TDDNN) architecture is used in this work, which is trained as an acoustic
model in an English Automatic Speech Recognition (ASR) task. A logistic
regression model is trained to classify the I-vectors. The proposed system is
tested with fourteen languages with various confusion pairs and it can be
easily extended to include a new language by just retraining the last simple
logistic regression model. The architectural flexibility is the major advantage
of the proposed system compared to the single DNN classifier based approach.
