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ProSarc introduces an audio-only framework that detects sarcasm by quantifying the mismatch between local prosodic dynamics and utterance-level emotional baselines. The model demonstrates strong generalization across spontaneous and cross-lingual datasets while providing interpretable uncertainty estimates that align with human perceptual ambiguity. This approach offers a robust alternative to frame-level labeling for temporal localization of sarcastic onsets in real-world speech scenarios.
ProSarc: Prosody-Aware Sarcasm Recognition Framework via Temporal Prosodic Incongruity
arXiv:2606.06168v1 Announce Type: new Abstract: We present ProSarc, an audio-only framework that detects sarcasm by modelling temporal prosodic incongruity, that is, the mismatch between local prosodic dynamics and the utterance-level emotional baseline. Dual encoding paths, a Global Emotion Encoder and a Temporal Prosody Encoder (BiLSTM + multi-head attention), feed a Prosodic Incongruity Analyzer that produces a scalar incongruity score for classification. Monte Carlo dropout provides uncertainty estimates, and an attention-based mechanism localises sarcastic onset without frame-level labels. ProSarc outperforms prior audio-only methods on MUStARD++ (F1=75.3) and generalises to spontaneous (PodSarc, F1=62.9) and cross-lingual speech (MuSaG, F1=65.6). Ten-run validation confirms the contribution of incongruity modelling (Wilcoxon p=0.002, Cohen's d=1.51). Human evaluation shows that model uncertainty tracks perceptual ambiguity and predicted onsets align with human-annotated temporal windows.
Abstract:We present ProSarc, an audio-only framework that detects sarcasm by modelling temporal prosodic incongruity, that is, the mismatch between local prosodic dynamics and the utterance-level emotional baseline. Dual encoding paths, a Global Emotion Encoder and a Temporal Prosody Encoder (BiLSTM + multi-head attention), feed a Prosodic Incongruity Analyzer that produces a scalar incongruity score for classification. Monte Carlo dropout provides uncertainty estimates, and an attention-based mechanism localises sarcastic onset without frame-level labels. ProSarc outperforms prior audio-only methods on MUStARD++ (F1=75.3) and generalises to spontaneous (PodSarc, F1=62.9) and cross-lingual speech (MuSaG, F1=65.6). Ten-run validation confirms the contribution of incongruity modelling (Wilcoxon p=0.002, Cohen's d=1.51). Human evaluation shows that model uncertainty tracks perceptual ambiguity and predicted onsets align with human-annotated temporal windows.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2606.06168 [cs.AI]
(or arXiv:2606.06168v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.06168 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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