# Improving Language Identification of Accented Speech

arXiv: https://arxiv.org/abs/2203.16972 · HTML: https://mlsanity.com/paper/2203.16972/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2022.

## Built on by (most cited first, 10 of 10)

- [Speech Robust Bench: A Robustness Benchmark For Speech Recognition](https://mlsanity.com/paper/2403.07937/) (2024)
- [Accidental Learners: Spoken Language Identification in Multilingual Self-Supervised Models](https://mlsanity.com/paper/2211.05103/) (2022)
- [A Compact End-to-End Model with Local and Global Context for Spoken Language Identification](https://mlsanity.com/paper/2210.15781/) (2022)
- [Confidence-based Ensembles of End-to-End Speech Recognition Models](https://mlsanity.com/paper/2306.15824/) (2023)
- [Investigating model performance in language identification: beyond simple error statistics](https://mlsanity.com/paper/2305.18925/) (2023)
- [LID Models are Actually Accent Classifiers: Implications and Solutions for LID on Accented Speech](https://mlsanity.com/paper/2506.00628/) (2025)
- [TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge](https://mlsanity.com/paper/2506.01458/) (2025)
- [On Barriers to Archival Audio Processing](https://mlsanity.com/paper/2507.08768/) (2025)
- [Geolocation-Aware Robust Spoken Language Identification](https://mlsanity.com/paper/2508.17148/) (2025)
- [Code-Switching Spoken Language Identification as Multi-Label Set Prediction](https://mlsanity.com/paper/2610.01450/) (2026)

## Abstract (arXiv)

Language identification from speech is a common preprocessing step in many spoken language processing systems. In recent years, this field has seen fast progress, mostly due to the use of self-supervised models pretrained on multilingual data and the use of large training corpora. This paper shows that for speech with a non-native or regional accent, the accuracy of spoken language identification systems drops dramatically, and that the accuracy of identifying the language is inversely correlated with the strength of the accent. We also show that using the output of a lexicon-free speech recognition system of the particular language helps to improve language identification performance on accented speech by a large margin, without sacrificing accuracy on native speech. We obtain relative error rate reductions ranging from to 35 to 63% over the state-of-the-art model across several non-native speech datasets.