ProSarc: Prosody-Aware Sarcasm Recognition Framework via Temporal Prosodic Incongruity

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Speech Processing · Depth: Expert, extended

Summary

ProSarc is an audio-only framework designed for sarcasm detection by modeling "temporal prosodic incongruity," which is the mismatch between local prosodic dynamics and an utterance's emotional baseline. The framework uses dual encoding paths: a Global Emotion Encoder and a Temporal Prosody Encoder (BiLSTM + multi-head attention), feeding into a Prosodic Incongruity Analyzer that generates a scalar incongruity score for classification. Monte Carlo dropout provides uncertainty estimates, and an attention mechanism localizes sarcastic onset without frame-level labels. ProSarc achieved an F1-score of 75.3 on MUStARD++, 62.9 on spontaneous speech (PodSarc), and 65.6 on cross-lingual speech (MuSaG), outperforming prior audio-only methods. Ten-run validation confirmed the incongruity modeling's contribution (Wilcoxon p=0.002, Cohen's d=1.51). Human evaluation showed model uncertainty tracks perceptual ambiguity, and predicted onsets align with human-annotated temporal windows, specifically the peak of sarcastic expression.

Key takeaway

For Machine Learning Engineers developing speech-based sentiment or intent detection, you should consider explicitly modeling temporal prosodic incongruity. This approach, demonstrated by ProSarc's F1-score of 75.3 on MUStARD++, significantly improves sarcasm detection from audio alone. Implement Monte Carlo dropout to quantify prediction uncertainty, allowing you to flag ambiguous cases for multimodal processing or human review, thereby enhancing system robustness and interpretability in real-world applications.

Key insights

Sarcasm can be robustly detected from audio by explicitly modeling temporal prosodic incongruity.

Principles

• Sarcasm manifests as local prosodic divergence from global emotional baseline.
• Larger self-supervised encoders yield lower predictive uncertainty.
• Audio-only sarcasm detection has an empirical modality ceiling.

Method

ProSarc uses dual encoders for global emotion and temporal prosody, fusing them via an incongruity analyzer to produce a scalar score for classification, with MC dropout for uncertainty.

In practice

• Use MC dropout to identify ambiguous predictions for human review.
• Combine global utterance statistics with frame-level prosodic dynamics.
• Consider multimodal fallback for samples with high audio-only uncertainty.

Topics

• Sarcasm Detection
• Speech Prosody
• Temporal Prosodic Incongruity
• Uncertainty Estimation
• Audio-only Models
• Self-supervised Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.