October 3, 2025Open Access

Spoken in Jest, Detected in Earnest: A Systematic Review of Sarcasm Recognition -- Multimodal Fusion, Challenges, and Future Prospects

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Authors

XGXiyuan Gao SNShekhar Nayak MCMatt Coler

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Implication

This review highlights advancements in sarcasm recognition using speech data and multimodal fusion methods, addressing challenges in cross-cultural contexts.

Key Points

The systematic review uncovers the significance of speech data in sarcasm recognition, enhancing interactions for individuals with neurodegenerative conditions.
Findings reveal limitations in current datasets and emphasize the evolution from traditional acoustic features to deep learning-based representations for better recognition.
Classification methods have progressed from unimodal approaches to advanced multimodal fusion techniques, improving the accuracy of sarcasm detection.
The study identifies a need for greater focus on multilingual and cross-cultural sarcasm recognition, highlighting sarcasm as a complex multimodal phenomenon.

Cite This Study

Gao et al. (2025) studied this question.

synapsesocial.com/papers/68e02f40f0e39f13e7fa2a94 https://doi.org/10.48550/arxiv.2509.04605

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Implication

This review highlights advancements in sarcasm recognition using speech data and multimodal fusion methods, addressing challenges in cross-cultural contexts.

Spoken in Jest, Detected in Earnest: A Systematic Review of Sarcasm Recognition -- Multimodal Fusion, Challenges, and Future Prospects

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Cite This Study

Spoken in Jest, Detected in Earnest: A Systematic Review of Sarcasm Recognition -- Multimodal Fusion, Challenges, and Future Prospects

Discussion

Member takes

Implication

Key Points

Cite This Study

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