Research
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models
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
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 relevant utterances in large speech collections so that they can be included in training data. One way to find such utterances is to transcribe the speech and detect language alternation in the text, but this depends on obtaining accurate multilingual transcriptions. We propose a lightweight method that detects CS directly from speech. It uses frame-level language information in pretrained speech…
Related
- Language Discrimination Improves Linguistic Learning in Multilingual Speech Models
- Contrastive Training with LLM-generated Near-Misses for Robust Code-Switching Speech Recognition
- Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR
- Towards Truly Multilingual ASR: Generalizing Code-Switching ASR to Unseen Language Pairs
Source: Apple ML Research | 2026-10-14