Language Recognition using Time Delay Deep Neural Network
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.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2018
- DOI:
- 10.48550/arXiv.1804.05000
- arXiv:
- arXiv:1804.05000
- Bibcode:
- 2018arXiv180405000S
- Keywords:
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- Electrical Engineering and Systems Science - Audio and Speech Processing;
- Computer Science - Sound
- E-Print:
- 5 pages, 1 figure, 1 table