# Spoken Language Identification using ConvNets

arXiv: https://arxiv.org/abs/1910.04269 · HTML: https://mlsanity.com/paper/1910.04269/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2109.13226/) (2021)
- [Universal Paralinguistic Speech Representations Using Self-Supervised Conformers](https://mlsanity.com/paper/2110.04621/) (2021)
- [LEAN: Light and Efficient Audio Classification Network](https://mlsanity.com/paper/2305.12712/) (2023)
- [Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions](https://mlsanity.com/paper/2104.11985/) (2021)
- [Binary Early-Exit Network for Adaptive Inference on Low-Resource Devices](https://mlsanity.com/paper/2206.09029/) (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 (LI) is an important first step in several speech processing systems. With a growing number of voice-based assistants, speech LI has emerged as a widely researched field. To approach the problem of identifying languages, we can either adopt an implicit approach where only the speech for a language is present or an explicit one where text is available with its corresponding transcript. This paper focuses on an implicit approach due to the absence of transcriptive data. This paper benchmarks existing models and proposes a new attention based model for language identification which uses log-Mel spectrogram images as input. We also present the effectiveness of raw waveforms as features to neural network models for LI tasks. For training and evaluation of models, we classified six languages (English, French, German, Spanish, Russian and Italian) with an accuracy of 95.4% and four languages (English, French, German, Spanish) with an accuracy of 96.3% obtained from the VoxForge dataset. This approach can further be scaled to incorporate more languages.