MiniSUPERB: Lightweight Benchmark for Self-supervised Speech Models

Understand

SUPERB was proposed to evaluate the generalizability of self-supervised learning (SSL) speech models across various tasks.

• However, it incurs high computational costs due to the large datasets and diverse tasks.
• In this paper, we introduce MiniSUPERB, a lightweight benchmark that efficiently evaluates SSL speech models with comparable results to SUPERB but lower computational costs significantly.
• We carefully select representative tasks, sample datasets, and extract model representations offline.