# Code-Switching Spoken Language Identification as Multi-Label Set Prediction

arXiv: https://arxiv.org/abs/2610.01450 · HTML: https://mlsanity.com/paper/2610.01450/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 27 references that are in the map.
- First posted on arXiv 1 Oct 2026.

## Builds on (most cited first, 21 of 21)

- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation](https://mlsanity.com/paper/2407.05361/) (2024)
- [XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model](https://mlsanity.com/paper/2406.04904/) (2024)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer](https://mlsanity.com/paper/2401.16658/) (2024)
- [YODAS: Youtube-Oriented Dataset for Audio and Speech](https://mlsanity.com/paper/2406.00899/) (2024)
- [A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies](https://mlsanity.com/paper/2301.01967/) (2023)
- [Reducing Language confusion for Code-switching Speech Recognition with Token-level Language Diarization](https://mlsanity.com/paper/2210.14567/) (2022)
- [OWSM v4: Improving Open Whisper-Style Speech Models via Data Scaling and Cleaning](https://mlsanity.com/paper/2506.00338/) (2025)
- [Aligning Speech to Languages to Enhance Code-switching Speech Recognition](https://mlsanity.com/paper/2403.05887/) (2024)
- [CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset](https://mlsanity.com/paper/2509.14161/) (2025)
- [Improving Language Identification of Accented Speech](https://mlsanity.com/paper/2203.16972/) (2022)
- [MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization](https://mlsanity.com/paper/2305.18881/) (2023)
- [Unified model for code-switching speech recognition and language identification based on a concatenated tokenizer](https://mlsanity.com/paper/2306.08753/) (2023)
- [Fine-Tuned Self-Supervised Speech Representations for Language Diarization in Multilingual Code-Switched Speech](https://mlsanity.com/paper/2312.09645/) (2023)
- [Code-Switched Language Identification is Harder Than You Think](https://mlsanity.com/paper/2402.01505/) (2024)
- [Joint Language Identification of Code-Switching Speech using Attention based E2E Network](https://mlsanity.com/paper/1907.06342/) (2019)
- [CS-YODAS: A Mined Dataset of In-the-Wild Code-Switched Speech](https://mlsanity.com/paper/2606.11514/) (2026)
- [Adversarial synthesis based data-augmentation for code-switched spoken language identification](https://mlsanity.com/paper/2205.15747/) (2022)

## Abstract (arXiv)

Code-switched (CS) speech leaks through the monolingual language identification (LID) filters used to curate massive speech corpora, calling for CS-aware LID (CS-LID). We formulate utterance-level CS-LID as multi-label language-set prediction and propose a set generator that directly outputs the languages in an utterance, comparing it against atomic-pair and score-based classification baselines. Oracle Top-k is the strongest baseline, but thresholding fails because no single threshold separates CS from monolingual speech. Our set generator predicts the correct language count on unseen pairs without assuming the number of languages, but underperforms oracle Top-k in exact set accuracy. Our analysis identifies the key obstacles to robust CS-LID: oracle cardinality, threshold instability, language bias in CS training data, and the synthetic-to-real gap.