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...
arXiv:2610.16601v1 Announce Type: cross
Abstract: Processing multilingual text requires both identifying a broad range of languages and distinguishing the language of each token in inputs containing...
By Seongtae Hong, Youngjoon Jang, Seungyoon Lee, Jungseob Lee, Heuiseok Lim
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. 11786v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems and audio language models (audio LMs) now report low error rates on monolingual benchmarks, but their behavior on code switched speech in low resource, diacritic rich languages remains poorly characterized.
By Chibuzor Okocha, Christan Earl Grant
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