# Language Identification Using Deep Convolutional Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1708.04811 · HTML: https://mlsanity.com/paper/1708.04811/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2017.

## Built on by (most cited first, 7 of 7)

- [LEAN: Light and Efficient Audio Classification Network](https://mlsanity.com/paper/2305.12712/) (2023)
- [Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning](https://mlsanity.com/paper/2305.19759/) (2023)
- [Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions](https://mlsanity.com/paper/2104.11985/) (2021)
- [Is Attention always needed? A Case Study on Language Identification from Speech](https://mlsanity.com/paper/2110.03427/) (2021)
- [Modernizing Open-Set Speech Language Identification](https://mlsanity.com/paper/2205.10397/) (2022)
- [Adversarial synthesis based data-augmentation for code-switched spoken language identification](https://mlsanity.com/paper/2205.15747/) (2022)
- [Low-Resource Spoken Language Identification Using Self-Attentive Pooling and Deep 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/2106.00052/) (2021)

## Abstract (arXiv)

Language Identification (LID) systems are used to classify the spoken language from a given audio sample and are typically the first step for many spoken language processing tasks, such as Automatic Speech Recognition (ASR) systems. Without automatic language detection, speech utterances cannot be parsed correctly and grammar rules cannot be applied, causing subsequent speech recognition steps to fail. We propose a LID system that solves the problem in the image domain, rather than the audio domain. We use a hybrid Convolutional Recurrent Neural Network (CRNN) that operates on spectrogram images of the provided audio snippets. In extensive experiments we show, that our model is applicable to a range of noisy scenarios and can easily be extended to previously unknown languages, while maintaining its classification accuracy. We release our code and a large scale training set for LID systems to the community.