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Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR
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Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR.
Key facts
- Their approach significantly advances CS-ASR systems, achieving notable Mix Error Rate (MER) reductions on SEAME’s devman (6.35%) and devsge (8.29%) subsets
- Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR
- Code-switching (CS), alternating languages within the same utterance, poses significant challenges for automatic speech recognition (ASR) due to limited CS training data
- This paper applies an iterative pseudo-labeling training approach to CS-ASR for the first time, demonstrating its effectiveness in leveraging unlabeled data to improve CS-ASR performance
Summary
Authors Qu Yang†**, Cakra Wardhana, Tim Ng. Code-switching (CS), alternating languages within the same utterance, poses significant challenges for automatic speech recognition (ASR) due to limited CS training data. Their approach significantly advances CS-ASR systems, achieving notable Mix Error Rate (MER) reductions on SEAME’s devman (6.35%) and devsge (8.29%) subsets.