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 addresses the challenge of identifying code‑switched utterances in language identification systems. It revisits the MaskLID approach, highlighting its overreliance on word‑level language association scores, and reformulates its optimization as an Integer Linear Program to incorporate clear, interpretable constraints. These enhancements significantly improve performance across ten diverse languages on code‑switching benchmarks, with the authors releasing code and data for reproducibility.
By Joanna Rado{\l}a, Josep Maria Crego, Fran\c{c}ois Yvon
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
AfriSwitch is a 61.36‑hour, human‑transcribed benchmark of in‑the‑wild code‑switched speech covering 16 African languages and varieties, annotated with switch‑level English span tags, per‑utterance Code‑Mixing Index (CMI), and switch‑point counts. The corpus reveals that code‑switching behaviour varies widely across languages, with no single metric fully capturing how code‑switched a language is. Benchmarking five open and commercial multilingual ASR systems in a zero‑shot setting shows high word error rates, with the best system averaging 35.93% WER and none dropping below 24% on any language, indicating that Africa‑targeted training rather than model scale or nominal language coverage best predicts performance.
By Gabrial Zencha Ashungafac, Busayo Awobade, Tobi Olatunji
arXiv:2609.01016v1 Announce Type: new
Abstract: Current speech synthesis struggles with code-switching, which mixes a foreign language phrase into a primary language utterance, causing the phrase to...
By Che Hyun Lee, Sangkwon Park, Donghun Kang, Dongwook Lee, Youngho Cho, Heeseung Kim, Sungroh Yoon
arXiv:2608. 04586v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved significant success in speech-to-text translation (S2TT).
By Yexing Du, Kaiyuan Liu, Youcheng Pan, Bo Yang, Chengpeng Fu, Yu Wang, Ming Liu