Disfluent Cues for Enhanced Speech Understanding in Large Language Models
Morteza Rohanian, Farhad Nooralahzadeh, Omid Rohanian, David Clifton, Michael Krauthammer
Abstract
In computational linguistics, the common practice is to “clean” disfluent content from spontaneous speech. However, we hypothesize that these disfluencies might serve as more than mere noise, potentially acting as informative cues. We use a range of pre-trained models for a reading comprehension task involving disfluent queries, specifically featuring different types of speech repairs. The findings indicate that certain disfluencies can indeed improve model performance, particularly those stemming from context-based adjustments. However, large-scale language models struggle to handle repairs involving decision-making or the correction of lexical or syntactic errors, suggesting a crucial area for potential improvement. This paper thus highlights the importance of a nuanced approach to disfluencies, advocating for their potential utility in enhancing model performance rather than their removal.- Anthology ID:
- 2023.findings-emnlp.238
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3676–3684
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.238
- DOI:
- 10.18653/v1/2023.findings-emnlp.238
- Cite (ACL):
- Morteza Rohanian, Farhad Nooralahzadeh, Omid Rohanian, David Clifton, and Michael Krauthammer. 2023. Disfluent Cues for Enhanced Speech Understanding in Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3676–3684, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Disfluent Cues for Enhanced Speech Understanding in Large Language Models (Rohanian et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.findings-emnlp.238.pdf