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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.
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.
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: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.
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...
arXiv:2607.25202v2 Announce Type: replace Abstract: Conversational entrainment is well-studied in monolingual and written contexts, but remains underexplored in spoken code-switching (CSW). We presen...