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. The approach comprises three phases: pseudo-label generation, two-stage bilingual model training, and iterative improvements. It begins by generating pseudo-labels from a large unlabeled corpus, creating a semi-supervised dataset. This datas
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- PossiblePossibly related (embedding) · 45%PacificAI/langtest →
- LinkedLinked via arxiv author · 85%Qu Yang →
“Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR”
- LinkedLinked via arxiv author · 85%Cakra Wardhana →
“Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR”
- LinkedLinked via arxiv author · 85%Tim Ng →
“Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR”
- FuzzySimilar title/name (fuzzy) · 87%CVHub520/X-AnyLabeling →
“Fuzzy title match (0.94): “Progressive Refinement: An Iterative Pseudo-Labeling Approac” ≈ “CVHub520/X-AnyLabeling””