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

Researchers introduce ProSarc, an audio-only machine learning framework that detects sarcasm by analyzing temporal mismatches between local prosodic patterns and overall emotional tone. The model achieves strong performance on multiple datasets (F1=75.3 on MUStARD++) and demonstrates cross-lingual generalization, advancing computational understanding of spoken sarcasm detection.

ProSarc represents a meaningful advance in conversational AI by tackling the nuanced problem of sarcasm detection through prosodic analysis. Traditional sentiment analysis systems struggle with sarcasm because the literal words often contradict the intended meaning; ProSarc solves this by measuring incongruity—the acoustic mismatch between what someone says and how they say it. This approach mirrors human perception: we detect sarcasm partly through tonal inconsistencies rather than semantic analysis alone.

The framework's architecture employs dual encoding paths that separately model global emotional context and local prosodic dynamics, combining these signals through an incongruity analyzer. The inclusion of uncertainty quantification via Monte Carlo dropout and attention-based localization of sarcastic onset without frame-level supervision demonstrates sophisticated engineering. Performance validation across MUStARD++, PodSarc, and MuSaG datasets—including spontaneous speech and cross-lingual samples—confirms genuine generalization rather than dataset-specific overfitting.

For the AI industry, this work has practical implications for voice assistants, customer service automation, and content moderation systems that currently fail to interpret sarcasm accurately. As conversational AI becomes increasingly deployed in customer-facing applications, sarcasm misinterpretation poses real usability problems. The cross-lingual capabilities are particularly valuable for global applications. The statistical validation (Wilcoxon p=0.002) and human evaluation studies establish credibility beyond typical academic benchmarking.

The research opens pathways for multimodal integration—combining audio prosody with visual cues and linguistic features—and for downstream applications in dialogue systems where understanding speaker intent prevents misguided automated responses. This positions audio-centric approaches as complementary to text-based NLP models in hybrid conversational systems.

• →ProSarc detects sarcasm by modeling temporal prosodic incongruity—the mismatch between local acoustic patterns and utterance-level emotional tone.
• →The framework achieves F1=75.3 on MUStARD++ and generalizes to spontaneous and cross-lingual speech, outperforming prior audio-only methods.
• →Dual encoding paths separate global emotion modeling from temporal prosody analysis, feeding an incongruity analyzer for classification.
• →Uncertainty quantification and attention-based onset localization provide interpretability without requiring frame-level training labels.
• →Statistical validation and human evaluation confirm that model predictions align with perceptual judgments of sarcasm and ambiguity.