SUPERB: Speech processing Universal PERformance Benchmark

TL;DR

SUPERB introduces a comprehensive benchmark and leaderboard for evaluating speech processing models across multiple tasks, emphasizing the importance of SSL representations and minimal task-specific modifications.

Contribution

It provides the first unified benchmark for speech processing, enabling systematic evaluation of shared SSL models across diverse tasks with minimal architecture changes.

Findings

SSL representations are highly generalizable across tasks

The proposed framework achieves competitive results with lightweight heads

SUPERB facilitates research in speech representation learning

Abstract

Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large volumes of unlabeled data and achieves state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the speech processing community lacks a similar setup to systematically explore the paradigm. To bridge this gap, we introduce Speech processing Universal PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. Among multiple usages of the shared model, we especially focus on extracting the representation learned from SSL due to its preferable re-usability. We present a simple framework to solve SUPERB tasks by learning task-specialized lightweight prediction heads on top of the frozen shared model. Our results demonstrate that the framework is promising as SSL representations show competitive generalizability and accessibility across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a benchmark toolkit to fuel the research in representation learning and general speech processing.