Apple Proposes Speech-Based Method for Code-Switching Detection
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models
Transcription-based CS discovery breaks down when ASR itself is weak in mixed-language speech, so detecting alternation straight from model representations sidesteps that bottleneck. Teams building speech models for multilingual markets — including Southeast Asia's heavily code-switched environments like Singlish and Taglish — can use this to mine training data at scale. Compared with text-based pipelines, it removes dependence on multilingual ASR quality.
Apple researchers published a method that detects code-switching (CS) — alternation between languages within one utterance — directly from audio rather than relying on text transcriptions. The approach uses frame-level language information from pretrained speech foundation models, exploiting language-selective dimensions in their representations. This enables finding CS utterances in large speech collections so they can be added to pretraining corpora, where CS is currently underrepresented. The method is described as lightweight and avoids the need for accurate multilingual transcription.
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 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…
