Linguistic Diversity Impact on Multilingual Self-Supervised Speech Models

Self-supervised learning (SSL) has transformed speech processing, yet its reliance on massive pre-training datasets remains a bottleneck. While robustness is often attributed to scale and diversity, the role of the data distribution is less understood. We systematically examine how curated subsets of pre-training data influence Automatic Speech Recognition (ASR) performance. Surprisingly, optimizing for acoustic, speaker, or linguistic diversity yields no clear improvements over random sampling. Instead, we find that prioritizing the longest utterances achieves superior ASR results while using

Research goal: How does the linguistic diversity of pre-training data influence the multilingual capabilities of self-supervised speech models, as evaluated by WER on the MLS and Babel benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.