Topic

Code-Switching

1 post about Code-Switching. Every summary links to the original.

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Apple
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models
Code-switching, the alternation between languages in a single utterance, is common in multilingual speech but underrepresented in training corpora for speech models. To address this, a lightweight method detects code-switching directly from speech without requiring multilingual transcriptions. The approach leverages language-selective dimensions in pretrained speech encoders by identifying hidden-state dimensions selective to each language from monolingual recordings and aggregating frame-level activations to predict code-switching. On the CS-FLEURS dataset covering 14 language pairs, the Whisper-small encoder achieves a mean F1 of 0.798, outperforming a CNN baseline (0.776) and a HuBERT linear baseline (0.751). These results show that pretrained speech encoders inherently encode the information necessary for code-switching detection, offering a scalable tool for building more representative multilingual corpora.

The best engineering reads, once a week.

Topic

1 post about Code-Switching. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Apple
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models
Code-switching, the alternation between languages in a single utterance, is common in multilingual speech but underrepresented in training corpora for speech models. To address this, a lightweight method detects code-switching directly from speech without requiring multilingual transcriptions. The approach leverages language-selective dimensions in pretrained speech encoders by identifying hidden-state dimensions selective to each language from monolingual recordings and aggregating frame-level activations to predict code-switching. On the CS-FLEURS dataset covering 14 language pairs, the Whisper-small encoder achieves a mean F1 of 0.798, outperforming a CNN baseline (0.776) and a HuBERT linear baseline (0.751). These results show that pretrained speech encoders inherently encode the information necessary for code-switching detection, offering a scalable tool for building more representative multilingual corpora.

The best engineering reads, once a week.