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Code-Switching ASR Training

1 post · Aug 2026

What this trail covers

This post introduces an iterative pseudo-labeling approach for Mandarin-English Code-Switching Automatic Speech Recognition (ASR). The method leverages unlabeled data to improve ASR performance by generating pseudo-labels, followed by a two-stage bilingual model training (pre-training and fine-tuning on supervised CS data), and iterative refinements. This approach significantly reduces Mix Error Rate (MER) on SEAME datasets.

All Apple trails
  1. Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASRThe post 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 dataset supports a two-stage training framework where the model is pre-trained and then fine-tuned on supervised CS data. Iterative refinements further enhance the model's accuracy in handling complex CS scenarios. Our approach significantly advances CS-ASR systems, achieving notable Mix Error Rate (MER) reductions on SEAME's devman (6.35%) and devsge (8.29%) subsets.