Detecting Domain-Specific Credibility and Expertise in Text and Speech

Shengli Hu


We investigate and explore the interplay of credibility and expertise level in text and speech. We collect a unique domain-specific multimodal dataset and analyze a set of acoustic-prosodic and linguistic features in both credible and less credible speech by professionals of varying expertise levels. Our analyses shed light on potential indicators of domain-specific perceived credibility and expertise, as well as the interplay in-between. Moreover, we build multimodal and multi-task deep learning models that outperform human performance by 6.2% in credibility and 3.8% in expertise level, building upon state-of-the-art self-supervised pre-trained language models. To our knowledge, this is the first multimodal multi-task study that analyzes and predicts domain-specific credibility and expertise level at the same time.1


 DOI: 10.21437/Interspeech.2020-1518

Cite as: Hu, S. (2020) Detecting Domain-Specific Credibility and Expertise in Text and Speech. Proc. Interspeech 2020, 4208-4212, DOI: 10.21437/Interspeech.2020-1518.


@inproceedings{Hu2020,
  author={Shengli Hu},
  title={{Detecting Domain-Specific Credibility and Expertise in Text and Speech}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={4208--4212},
  doi={10.21437/Interspeech.2020-1518},
  url={http://dx.doi.org/10.21437/Interspeech.2020-1518}
}