1 citations · 1 across the 10 of their papers we have counts for
10 papers
What Makes Synthetic Speech Sound Sarcastic? A Prosody-Controlled Perception Study
Zhu Li, Shekhar Nayak, Matt Coler
Prosody plays an important role in sarcasm perception, yet previous studies have relied on naturally produced speech that lacks fine-grained control over individual acoustic dimens…
Forgive or forget: Understanding the context of hate in audio retrieval systems
Arghya Pal, Sailaja Rajanala, Raphael C. -W. Phan +1
Handling toxic retrieval in text-to-audio systems is challenging due to contextual dependencies. Existing strategies (e.g., rephrasing, summarization) risk altering intent or omitt…
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…
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)…
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…
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…
AMuSeD: An Attentive Deep Neural Network for Multimodal Sarcasm Detection Incorporating Bi-modal Data Augmentation
Xiyuan Gao, Shubhi Bansal, Kushaan Gowda +4
Detecting sarcasm effectively requires a nuanced understanding of context, including vocal tones and facial expressions. The progression towards multimodal computational methods in…
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…