Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection

ArXi:2506.00955v2 Announce Type: replace 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 rely on multimodal data, limiting their applicability in contexts where only speech is available. To address this, we propose an annotation pipeline that leverages large language models (LLMs) to generate a sarcasm dataset.