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Apple develops speech-based code-switching detection for multilingual corpora

By PulseAugur Editorial · [1 sources] ·

Apple Machine Learning Research has developed a novel method for detecting code-switching in speech without requiring transcriptions. This technique leverages language-selective dimensions within pretrained speech encoders, such as Whisper-small, to identify utterances containing multiple languages. The approach achieved a mean F1 score of 0.798 across 14 language pairs on the CS-FLEURS dataset, outperforming other baseline models. This lightweight tool can aid in building more representative multilingual corpora by efficiently finding code-switching instances in large speech collections. AI

IMPACT Enables more efficient creation of multilingual datasets, potentially improving performance of speech and NLP models across languages.

RANK_REASON The item describes a research paper detailing a new method for code-switching detection in speech, published by Apple's Machine Learning Research group. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Apple Machine Learning Research →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Apple develops speech-based code-switching detection for multilingual corpora

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The item describes a research paper detailing a new method for code-switching detection in speech, published by Apple's Machine Learning Research group. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Apple Machine Learning Research TIER_1 English(EN) ·

    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 traini…

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