The paper introduces a lightweight approach to detect code‑switching (CS) directly from speech by leveraging language‑selective hidden‑state dimensions in pretrained speech encoders. Using monolingual recordings to identify these dimensions, the method aggregates frame‑level activations to predict CS presence, achieving a mean F1 score of 0.798 on the CS‑FLEURS dataset with the Whisper‑small encoder. This demonstrates that pretrained speech models inherently encode CS information, offering a practical tool for expanding multilingual corpora.
By Jie Chi, Zakaria Aldeneh, Masha Fedzechkina, Maureen de Seyssel, Natalie Schluter
Code-switching (CS), the alternation between languages within an utterance, is common in multilingual speech but underrepresented in pretraining corpora. Increasing CS coverage requires finding releva...
The paper surveys how code‑mixed (CM) text and speech systems transfer knowledge between CM data and the native languages involved, a process that can go in both directions. It introduces a taxonomy of transfer direction, route, and modality, and finds that most work transfers from native languages to CM, while the reverse is newer and mainly aligns the two languages. The study notes that text and speech methods overlap but rarely cite each other, and that native data often outperforms new architectures on shared speech benchmarks, highlighting open questions about data mixing and evaluation across scripts.
By Debajyoti Mazumder, Akash Anil
arXiv:2510. 05678v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have achieved notable progress in multilingual settings, their performance remains uneven across languages as LLMs often rely on English-centric latent representations.
By Haneul Yoo, Jiho Jin, Kyunghyun Cho, Alice Oh
arXiv:2606. 19381v1 Announce Type: cross Abstract: Code-switch (CS) Automatic Speech Recognition (ASR) remains challenging due to limited availability of high quality CS text-speech pairs for training.
By Yue Heng Yeo, Haoyang Li, Yizhou Peng, Shreyas Gopal, Hexin Liu, Leibny Paola Garcia-Perera, Hardik B. Sailor, Jeremy H. M. Wong, Eng Siong Chng
The paper investigates whether training small decoder-only transformers on code‑switched text can induce cross‑lingual alignment. Using two 100‑million‑word multilingual corpora—one a mix of English, Dutch, and Chinese BabyBabelLM data, and another generated by inserting word‑ and sentence‑level code‑switching via an LLM—the authors find that code‑switched training aligns representations of parallel text, especially across different scripts, and that this alignment persists when later training on monolingual documents. A curriculum that progresses from word‑level code‑switching to sentence‑level code‑switching and finally to monolingual data yields models that outperform baselines on the BabyLM evaluation suite, demonstrating that code‑switching curriculum learning is an effective data augmentation strategy for multilingual pretraining.
By Dries Rooryck, Alex Cai, Yonatan Belinkov, David Alvarez-Melis, Kiant\'e Brantley
Systems for code-mixed (CM) text and speech rarely start from scratch. CM data is scarce, so most work borrows from the languages being mixed, which we call native languages. This transfer can also ru...
Dialectal variation remains a major challenge for multilingual language models. Perturbation-based continued pre-training (CPT) has emerged as a promising approach to improving robustness, yet existing work largely evaluates individual perturbation strategies in isolation and provides limited insight into why they work.
Audio large language models (Audio LLMs) often fail to transcribe English‑Mandarin code‑switching speech, exhibiting language omission, translation‑instead‑of‑transcription, and hallucination. By applying Direct Preference Optimization (DPO) with 100 K preference pairs, the models learn to preserve mixed‑language content rather than translate, leading to significant reductions in mixed‑error rates (up to 89.6% in‑distribution). The study demonstrates that DPO can effectively align multilingual Audio LLMs for accurate code‑switching transcription.
By Trung Nguyen Quang, Cheng Yi Lewis Won, Minh Duc Pham, Yingxu He, Shuo Sun, Ai Ti Aw
The paper investigates how multilingual large language models can unintentionally switch languages during generation. It compares three techniques—ValSel, FreqSel, and AnnSel—for pinpointing latent variables that control language choice in cross‑layer transcoders. Using new multilingual benchmarks and targeted interventions on Gemma‑2‑2B and Qwen3‑4B, the study finds all methods can steer output language, with FreqSel performing best and AnnSel providing interpretable selections via explicit annotations.
By Ryo Mitsuhashi, Sabri Boughorbel, Majd Hawasly
arXiv:2610.09321v1 Announce Type: new
Abstract: Speech Language Model (SLM) performance often degrades on dialects due to data scarcity. Conventional text-to-speech (TTS) augmentation struggles to co...
By Shunsuke Mitsumori, Tomoya Mizumoto, Yusuke Fujita
arXiv:2609.14231v1 Announce Type: cross
Abstract: Controllable synthesis of nonverbal vocalizations (NVVs) is es- sential for natural and expressive speech, but remains challeng- ing due to their aco...
By Ziyu Zhang, Yun Chen, Taihui Wang, Hanzhao Li, Qicong Xie, Rilin Chen, Zhixian Zhao, Lei Xie