CONF Rangappa_INTERSPEECH_2025/IDIAP Efficient Data Selection for Domain Adaptation of ASR Using Pseudo-Labels and Multi-Stage Filtering Rangappa, Pradeep Carofilis, Andrés Prakash, Jeena Kumar, Shashi Burdisso, Sergio Madikeri, Srikanth Villatoro-Tello, Esaú Sharma, Bidisha Motlicek, Petr Hacioğlu, Kadri Venkatesan, Shankar Vyas, Saurabh Stolcke, Andreas Data Selection speech recognition whisper Zipformer EXTERNAL http://publications.idiap.ch/attachments/papers/2025/Rangappa_INTERSPEECH_2025.pdf PUBLIC Proc. Interspeech 2025 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.