新闻
Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR
Apple Machine Learning Research · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Apple researchers applied iterative pseudo-labeling to improve speech recognition for Mandarin-English code-switching, reducing error rates by 6-8 percent.
- 为何重要
- Speech engineers building multilingual ASR systems need this when training data for code-switched speech is scarce or expensive to label.
- 注意
- Results are specific to Mandarin-English on SEAME datasets; effectiveness on other language pairs or domains remains unclear from this work.
收听本摘要
- rag
- speech
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