1 citations · 1 across the 3 of their papers we have counts for
8 papers
Modeling Sarcastic Speech: Semantic and Prosodic Cues in a Speech Synthesis Framework
Zhu Li, Yuqing Zhang, Xiyuan Gao +2
Sarcasm is a pragmatic phenomenon in which speakers convey meanings that diverge from literal content, relying on an interaction between semantics and prosodic expression. However,…
Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection
Zhu Li, Yuqing Zhang, Xiyuan Gao +2
Sarcasm fundamentally alters meaning through tone and context, yet detecting it in speech remains a challenge due to data scarcity. In addition, existing detection systems often re…
SarcasmMiner: A Dual-Track Post-Training Framework for Robust Audio-Visual Sarcasm Reasoning
Zhu Li, Yongjian Chen, Huiyuan Lai +3
Multimodal sarcasm detection requires resolving pragmatic incongruity across textual, acoustic, and visual cues through cross-modal reasoning. To enable robust sarcasm reasoning wi…
Evaluating Multimodal Large Language Models on Spoken Sarcasm Understanding
Zhu Li, Xiyuan Gao, Yuqing Zhang +2
Sarcasm detection remains a challenge in natural language understanding, as sarcastic intent often relies on subtle cross-modal cues spanning text, speech, and vision. While prior…
Spoken in Jest, Detected in Earnest: A Systematic Review of Sarcasm Recognition -- Multimodal Fusion, Challenges, and Future Prospects
Xiyuan Gao, Shekhar Nayak, Matt Coler
Sarcasm, a common feature of human communication, poses challenges in interpersonal interactions and human-machine interactions. Linguistic research has highlighted the importance…
Integrating Feedback Loss from Bi-modal Sarcasm Detector for Sarcastic Speech Synthesis
Zhu Li, Yuqing Zhang, Xiyuan Gao +4
Sarcastic speech synthesis, which involves generating speech that effectively conveys sarcasm, is essential for enhancing natural interactions in applications such as entertainment…