用迭代伪标签提升中英混用语音识别，显著降低错误率。
Progressive Refinement: An Iterative Pseudo-Labeling Approach for Mandarin-English Code-Switching ASR
- 通过无标签数据生成伪标签，构建半监督训练集。
- 在SEAME数据集上，混合错误率分别降低6.35%和8.29%。
- 适合缺乏标注数据的多语言语音识别研究者。
中英混用（CS）语音识别因标注数据稀缺面临挑战。本文首次提出一种迭代伪标签训练方法，有效利用无标签数据提升模型性能。流程包括：从大规模无标签语料生成伪标签，构建半监督数据集；采用两阶段双语模型训练（预训练+微调）；通过迭代优化增强复杂混用场景的识别能力。实验表明，该方法在SEAME数据集的devman子集（6.35%）和devsge子集（8.29%）上实现显著的混合错误率下降，推动了中文-英文混用语音识别系统的发展。
原文摘要 · Abstract (English)
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 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.
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