用伪标签筛选100小时高质量数据，让小团队也能高效适配语音识别。
Efficient Data Selection for Domain Adaptation of ASR Using Pseudo-Labels and Multi-Stage Filtering
- 结合WER、NER和字符错误率，多阶段过滤伪标签数据。
- 仅用100小时（1.4%）数据就达到7500小时的识别效果（12.3% WER）。
- 适合资源有限但需快速部署领域化语音模型的团队。
针对小规模组织在有限标注数据和计算资源下微调预训练语音识别模型的挑战，本文探索不同数据筛选流程，提出一种基于Whisper（编码器-解码器）与Zipformer（转换器）生成伪标签的鲁棒筛选方法。该方法融合词错误率（WER）预测、命名实体识别（NER）及字符错误率（CER）分析，提取高质量训练片段。在7500小时基准数据上评估，使用伪标签微调后，系统在呼叫中心数据上实现12.3%的词错误率；经多阶段过滤，数据量缩减至100小时（占原数据1.4%），性能保持不变；类似趋势也在Fisher English数据集上观察到。
原文摘要 · Abstract (English)
Fine-tuning pretrained ASR models for specific domains is challenging for small organizations with limited labeled data and computational resources. Here, we explore different data selection pipelines and propose a robust approach that improves ASR adaptation by filtering pseudo-labels generated using Whisper (encoder-decoder) and Zipformer (transducer) models. Our approach integrates multiple selection strategies -- including word error rate (WER) prediction, named entity recognition (NER), and character error rate (CER) analysis -- to extract high-quality training segments. We evaluate our method on Whisper and Zipformer using a 7500-hour baseline, comparing it to a CER-based approach relying on hypotheses from three ASR systems. Fine-tuning on 7500 hours of pseudo-labeled call center data achieves 12.3% WER, while our filtering reduces the dataset to 100 hours (1.4%) with similar performance; a similar trend is observed on Fisher English.
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