# SIGTYP 2021 Shared Task: Robust Spoken Language Identification

arXiv: https://arxiv.org/abs/2106.03895 · HTML: https://mlsanity.com/paper/2106.03895/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/1910.10261/) (2019)
- [Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification](https://mlsanity.com/paper/2010.11973/) (2020)
- [Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition](https://mlsanity.com/paper/1803.04567/) (2018)
- [Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions](https://mlsanity.com/paper/2104.11985/) (2021)

## Built on by (most cited first, 5 of 5)

- [Multilingual Gradient Word-Order Typology from Universal Dependencies](https://mlsanity.com/paper/2402.01513/) (2024)
- [Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity](https://mlsanity.com/paper/2111.01326/) (2021)
- [PWESuite: Phonetic Word Embeddings and Tasks They Facilitate](https://mlsanity.com/paper/2304.02541/) (2023)
- [Automated speech tools for helping communities process restricted-access corpora for language revival efforts](https://mlsanity.com/paper/2204.07272/) (2022)
- [Robust Language Identification for Romansh Varieties](https://mlsanity.com/paper/2603.15969/) (2026)

## Abstract (arXiv)

While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task. For many low-resource and endangered languages this is in part due to resource availability: where larger datasets exist, they may be single-speaker or have different domains than desired application scenarios, demanding a need for domain and speaker-invariant language identification systems. This year's shared task on robust spoken language identification sought to investigate just this scenario: systems were to be trained on largely single-speaker speech from one domain, but evaluated on data in other domains recorded from speakers under different recording circumstances, mimicking realistic low-resource scenarios. We see that domain and speaker mismatch proves very challenging for current methods which can perform above 95% accuracy in-domain, which domain adaptation can address to some degree, but that these conditions merit further investigation to make spoken language identification accessible in many scenarios.