This dataset is a large-scale audio-text benchmark dataset. As the first multi-dimensional trust evaluation benchmark tailored for large audio models, AudioTrust focuses on evaluating the multi-dimensional credibility of audio large language models (ALLMs). The relevant paper results are:AudioTrust: Benchmarking the Multifaceted Trustworthiness of Audio Large Language Models". Dataset overview:

• Contains 4,420+ real-scene audio-text data, covering 18 real-world scenarios such as daily conversations, emergency calls, and voice assistants.
• Task type: audio anti-spoofing detection, voice command recognition, content authenticity verification, multimodal semantic alignment.
• Modality: Audio (voice instructions, deceptive audio), Text (instruction description, evaluation label).

Citation

@misc{audiotrust2025, title={AudioTrust: Benchmarking the Multifaceted Trustworthiness of Audio Large Language Models}, author={ Kai Li and Can Shen and Yile Liu and Jirui Han and Kelong Zheng and Xuechao Zou and Lionel Z. Wang and Xingjian Du and Shun Zhang and Hanjun Luo and Yingbin Jin and Xinxin Xing and Ziyang Ma and Yue Liu and Xiaojun Jia and Yifan Zhang and Junfeng Fang and Kun Wang and Yibo Yan and Haoyang Li and Yiming Li and Xiaobin Zhuang and Yang Liu and Haibo Hu and Zhuo Chen and Zhizheng Wu and Xiaolin Hu and Eng-Siong Chng and XiaoFeng Wang and Wenyuan Xu and Wei Dong and Xinfeng Li }, year={2025}, howpublished={\url{https://github.com/JusperLee/AudioTrust}}, }