# Language Recognition using Time Delay Deep Neural Network

arXiv: https://arxiv.org/abs/1804.05000 · HTML: https://mlsanity.com/paper/1804.05000/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [Implicit Self-supervised Language Representation for Spoken Language Diarization](https://mlsanity.com/paper/2308.10470/) (2023)

## Abstract (arXiv)

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.