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Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR
Apple Machine Learning Research · Published · 3 min read
In 30 seconds
- What happened
- Apple researchers applied iterative pseudo-labeling to improve speech recognition for Mandarin-English code-switching, reducing error rates by 6-8 percent.
- Why it matters
- Speech engineers building multilingual ASR systems need this when training data for code-switched speech is scarce or expensive to label.
- Watch out
- Results are specific to Mandarin-English on SEAME datasets; effectiveness on other language pairs or domains remains unclear from this work.
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