  Text language models have shown remarkable zero-shot capability in
generalizing to unseen tasks when provided with well-formulated instructions.
However, existing studies in speech processing primarily focus on limited or
specific tasks. Moreover, the lack of standardized benchmarks hinders a fair
comparison across different approaches. Thus, we present Dynamic-SUPERB, a
benchmark designed for building universal speech models capable of leveraging
instruction tuning to perform multiple tasks in a zero-shot fashion. To achieve
comprehensive coverage of diverse speech tasks and harness instruction tuning,
we invite the community to collaborate and contribute, facilitating the dynamic
growth of the benchmark. To initiate, Dynamic-SUPERB features 55 evaluation
instances by combining 33 tasks and 22 datasets. This spans a broad spectrum of
dimensions, providing a comprehensive platform for evaluation. Additionally, we
propose several approaches to establish benchmark baselines. These include the
utilization of speech models, text language models, and the multimodal encoder.
Evaluation results indicate that while these baselines perform reasonably on
seen tasks, they struggle with unseen ones. We release all materials to the
public and welcome researchers to collaborate on the project, advancing
technologies in the field together.
