# Apple — Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models

- Company: Apple (apple.com)
- Announced: 2026-10-14
- Category: research-paper
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://machinelearning.apple.com/research/code-switching-detection
- Record: https://forck.live/items/22247-code-switching-detection-via-language-selective-dimensions-in-speech
- Subject: Machine Learning Research
- Models affected: Whisper-small

Apple researchers propose a lightweight method to detect code-switching directly from speech using frame-level language information in pretrained speech encoders. The Whisper-small encoder achieves a mean F1 of 0.798 across 14 language pairs in CS-FLEURS, outperforming CNN and HuBERT baselines.

## Evidence

Verbatim from https://machinelearning.apple.com/research/code-switching-detection:

> We propose a lightweight method that detects CS directly from speech. It uses frame-level language information in pretrained speech encoders to determine whether both languages are present within an utterance.

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Record: https://forck.live/items/22247-code-switching-detection-via-language-selective-dimensions-in-speech
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