1 citations · 2 across the 6 of their papers we have counts for
6 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,…
Evaluating Multimodal Large Language Models on Spoken Sarcasm Detection
Zhu Li, Xiyuan Gao, Yuqing Zhang +2
Sarcasm detection remains challenging because sarcastic intent often depends on information across text, speech, and vision. We systematically evaluate large language models (LLMs)…
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…
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…
A Functional Trade-off between Prosodic and Semantic Cues in Conveying Sarcasm
Zhu Li, Xiyuan Gao, Yuqing Zhang +2
This study investigates the acoustic features of sarcasm and disentangles the interplay between the propensity of an utterance being used sarcastically and the presence of prosodic…
Improving Prosody for Unseen Texts in Speech Synthesis by Utilizing Linguistic Information and Noisy Data
Zhu Li, Yuqing Zhang, Mengxi Nie +4
Recent advancements in end-to-end speech synthesis have made it possible to generate highly natural speech. However, training these models typically requires a large amount of high…