arXiv:2606.06168v1 Announce Type: cross 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 labe
The proliferation of advanced AI models has driven a demand for more nuanced understanding of human communication, where sarcasm detection is a significant challenge.
Accurate sarcasm detection is crucial for improving AI's natural language understanding, refining sentiment analysis, and enabling more sophisticated human-AI interaction in various applications.
This framework offers a novel, audio-only method for identifying sarcasm, potentially leading to more robust and less context-dependent sarcasm recognition systems than textual or multi-modal approaches.
- · AI developers
- · Customer service platforms
- · Social media analytics
- · Conversational AI
- · AI systems relying solely on textual analysis for sentiment
- · Developers of less nuanced sarcasm detection models
AI models will gain a more sophisticated understanding of human communication nuances, particularly in spoken language.
Improved sarcasm detection can enhance content moderation, reduce misinterpretation in virtual assistants, and refine targeted advertising.
The ability to accurately interpret complex emotions like sarcasm could pave the way for more emotionally intelligent AI, eventually influencing human perception of AI sentience.
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