Language Recognition using Time Delay Deep Neural Network

2018/04/13 by Mousmita Sarma, Sarma, Mousmita, Kandarpa Kumar Sarma +3
Computer Science · #Speech Recognition and Synthesis #Natural Language Processing Techniques #Handwritten Text Recognition Techniques

paper · pdf · doi:10.48550/arxiv.1804.05000

Abstract

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.

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