Dynamic Superb Eval

---
name: dynamic-superb-eval
description: Evaluates instruction-tuned speech models on their ability to perform diverse speech and audio tasks using natural language instructions. It probes zero-shot generalization by testing performance on seen versus unseen tasks and instructions across six dimensions: content, speaker, semantics, degradation, paralinguistics, and audio. Use when the user wants to benchmark on Dynamic-SUPERB, or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2309.09510
  bibtex_key: [REDACTED]
  confidence: high
---

# dynamic-superb-eval

> Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech — Huang et al. (2023) (arXiv:2309.09510, 2023)

## What this evaluates

Evaluates instruction-tuned speech models on their ability to perform diverse speech and audio tasks using natural language instructions. It probes zero-shot generalization by testing performance on seen versus unseen tasks and instructions across six dimensions: content, speaker, semantics, degradation, paralinguistics, and audio.

## Datasets

- **Dynamic-SUPERB** — total 55; splits: test (55); repo https://github.com/dynamic-superb/dynamic-superb

## Metrics

- `accuracy` **(primary)** — range: percent
  - Standard classification accuracy: the percentage of instances where the model's generated output exactly matches the ground-truth label among the provided options.

## Input / output format

**Input**: Audio waveform paired with a natural language instruction that specifies the task and provides a set of candidate answer options.

**Output**: A generated text string corresponding to one of the candidate options provided in the instruction.

## Scoring recipe

```python
correct = 0
for pred, gold in zip(predictions, gold_labels):
    if pred.strip().lower() == gold.strip().lower():
        correct += 1
accuracy = (correct / len(predictions)) * 100
```

## Common pitfalls

- Models may generate outputs not present in the candidate options, causing accuracy to drop below the random baseline.
- Models often rely on superficial instruction patterns (bag-of-words) rather than semantic understanding, leading to poor generalization on unseen tasks.
- The random baseline is constructed by sampling from the label distribution of each task, not a uniform random guess.

## Evidence (verbatim from paper)

> Table 2 presents the evaluation results for each baseline on seen tasks in Dynamic-SUPERB. Due to space constraints, we report the average accuracy for each dimension.

## Citation

```bibtex
@misc{huang2023dynamicsuperb,
  title={Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech},
  author={Huang et al. (2023)},
  year={2023},
  note={arXiv:2309.09510}
}
```

- arXiv: 2309.09510