The foundation model paradigm leverages a shared foundation model to achieve
state-of-the-art (SOTA) performance for various tasks, requiring minimal
downstream-specific modeling and data annotation. This approach has proven
crucial in the field of Natural Language Processing (NLP). However, the speech
processing community lacks a similar setup to explore the paradigm
systematically. In this work, we establish the Speech processing Universal
PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for
speech. We propose a unified multi-tasking framework to address speech
processing tasks in SUPERB using a frozen foundation model followed by
task-specialized, lightweight prediction heads. Combining our results with
community submissions, we verify that the foundation model paradigm is
promising for speech, and our multi-tasking framework is simple yet effective,
as the best-performing foundation model shows competitive generalizability
across most SUPERB tasks. For reproducibility and extensibility, we have
developed a long-term maintained platform that enables deterministic
benchmarking, allows for result sharing via an online leaderboard, and promotes
collaboration through a community-driven benchmark database to support new
development cycles. Finally, we conduct a series of analyses to offer an
in-depth understanding of SUPERB and speech foundation models, including
information flows across tasks inside the models, the correctness of the
weighted-sum benchmarking protocol and the statistical significance and
robustness of the benchmark.

我们建立了语音处理通用性能基准（SUPERB），使用冻结的基础模型和任务专门化的轻量级预测头，证实了基础模型范式在语音处理中的潜力和我们的多任务框架的简单而有效性，具备竞争力的泛化能力。我们还进行了一系列分析，深入了解 SUPERB 和语音基准模型，包括模型内任务之间的信息流动，加权求和基准测试协议的正确性以及基准测试的统计显著性和稳健性。