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---
dataset_info:
- config_name: role_playing
features:
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dtype: int64
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- config_name: voice_instruction_following
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configs:
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data_files:
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path: role_playing/test-*
- config_name: voice_instruction_following
data_files:
- split: test
path: voice_instruction_following/test-*
---
# StyleSet
**WARNING**: This dataset contains some profane words.
**A spoken language benchmark for evaluating speaking-style-related speech generation**
Released in our paper, [Audio-Aware Large Language Models as Judges for Speaking Styles](https://arxiv.org/abs/2506.05984)
This dataset is released by NTU Speech Lab under the MIT license.
---
## Tasks
1. **Voice Style Instruction Following**
- Reproduce a given sentence verbatim.
- Match specified prosodic styles (emotion, volume, pace, emphasis, pitch, non-verbal cues).
2. **Role Playing**
- Continue a two-turn dialogue prompt in character.
- Generate the next utterance with appropriate prosody and style.
- The dataset is modified from IEMOCAP with the consent of the authors. Please refer to [IEMOCAP](https://sail.usc.edu/iemocap/) for details and the original data of IEMOCAP. We do not redistribute the data here.
---
## Evaluation
We use ALLM-as-a-judge for evaluation. Currently, we found that `gemini-2.5-pro-0506` reaches the best agreement with human evaluators.
The complete evaluation prompt and evaluation pipelines can be found in Table 3 to Table 5 in our paper.
## Citation
If you use StyleSet or find ALLM-as-a-judge useful, please cite our paper by
```
@misc{chiang2025audioawarelargelanguagemodels,
title={Audio-Aware Large Language Models as Judges for Speaking Styles},
author={Cheng-Han Chiang and Xiaofei Wang and Chung-Ching Lin and Kevin Lin and Linjie Li and Radu Kopetz and Yao Qian and Zhendong Wang and Zhengyuan Yang and Hung-yi Lee and Lijuan Wang},
year={2025},
eprint={2506.05984},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/2506.05984},
}
``` |