# LID Models are Actually Accent Classifiers: Implications and Solutions for LID on Accented Speech

arXiv: https://arxiv.org/abs/2506.00628 · HTML: https://mlsanity.com/paper/2506.00628/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 14 of 14)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [Quantifying Bias in Automatic Speech Recognition](https://mlsanity.com/paper/2103.15122/) (2021)
- [The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR](https://mlsanity.com/paper/2303.18110/) (2023)
- [Do self-supervised speech models develop human-like perception biases?](https://mlsanity.com/paper/2205.15819/) (2022)
- [The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and Methods](https://mlsanity.com/paper/2102.10233/) (2021)
- [Improving Language Identification of Accented Speech](https://mlsanity.com/paper/2203.16972/) (2022)
- [PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification](https://mlsanity.com/paper/2203.12366/) (2022)
- [Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses](https://mlsanity.com/paper/2006.00703/) (2020)
- [Investigating model performance in language identification: beyond simple error statistics](https://mlsanity.com/paper/2305.18925/) (2023)

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

- [Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data](https://mlsanity.com/paper/2603.07534/) (2026)

## Abstract (arXiv)

Prior research indicates that LID model performance significantly declines on accented speech; however, the specific causes, extent, and characterization of these errors remain under-explored. (i) We identify a common failure mode on accented speech whereby LID systems often misclassify L2 accented speech as the speaker's native language or a related language. (ii) We present evidence suggesting that state-of-the-art models are invariant to permutations of short spans of speech, implying they classify on the basis of short phonotactic features indicative of accent rather than language. Our analysis reveals a simple method to enhance model robustness to accents through input chunking. (iii) We present an approach that integrates sequence-level information into our model without relying on monolingual ASR systems; this reduces accent-language confusion and significantly enhances performance on accented speech while maintaining comparable results on standard LID.