1 citations · 1 across the 13 of their papers we have counts for
13 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…
Improving low-resource ASR using bilingual fine-tuning with language identification: a cross-linguistic evaluation
Reihaneh Amooie, Yun Hao, Wietse de Vries +3
This study explores how bilingual fine-tuning affects automatic speech recognition (ASR) in low-resource languages. We evaluate this method across nine linguistically and geographi…
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…
Overcoming Data Scarcity in Multi-Dialectal Arabic ASR via Whisper Fine-Tuning
Ömer Tarik Özyilmaz, Matt Coler, Matias Valdenegro-Toro
Although commercial Arabic automatic speech recognition (ASR) systems support Modern Standard Arabic (MSA), they struggle with dialectal speech. We investigate the effect of fine-t…
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…
Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance
Reihaneh Amooie, Wietse de Vries, Yun Hao +3
Automatic Speech Recognition (ASR) performance for low-resource languages is still far behind that of higher-resource languages such as English, due to a lack of sufficient labeled…
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…
Meta Learning Text-to-Speech Synthesis in over 7000 Languages
Florian Lux, Sarina Meyer, Lyonel Behringer +5
In this work, we take on the challenging task of building a single text-to-speech synthesis system that is capable of generating speech in over 7000 languages, many of which lack s…