Prioritizing longest utterances in SSL speech pre-training data outperforms random or diversity-based sampling for ASR performance while using half the data volume.
In contrast, selecting subsets of longer utterances consistently leads to lower WER, despite these subsets being most out-of-distribution relative to the fine-tuning data
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A Study of Data Selection Strategies for Pre-training Self-Supervised Speech Models
Prioritizing longest utterances in SSL speech pre-training data outperforms random or diversity-based sampling for ASR performance while using half the data volume.