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This research presents a computational framework that integrates LLaMA 3-based semantic analysis with prosodic extraction to improve the recognition and synthesis of sarcastic speech. The study demonstrates how combining discourse-level intent markers with acoustic delivery cues enhances both objective F1 scores and subjective listener ratings for pragmatic interpretation. These findings advance our understanding of multimodal AI models in handling complex, non-literal human communication patterns.
Modeling Sarcastic Speech: Semantic and Prosodic Cues in a Speech Synthesis Framework
arXiv:2510.07096v3 Announce Type: replace Abstract: Sarcasm is a pragmatic phenomenon in which speakers convey meanings that diverge from literal content, relying on an interaction between semantics and prosodic expression. However, how these cues jointly contribute to the recognition of sarcasm remains poorly understood. We propose a computational framework that models sarcasm as the integration of semantic interpretation and prosodic realization. Semantic cues are derived from an LLaMA 3 model fine-tuned to capture discourse-level markers of sarcastic intent, while prosodic cues are extracted through semantically aligned utterances drawn from a database of sarcastic speech, providing prosodic exemplars of sarcastic delivery. Using a speech synthesis testbed, perceptual evaluations show that semantic and prosodic cues enhance perceived sarcasm, with the combined system achieving the best downstream F1 while maintaining high subjective sarcasm ratings. These findings highlight the complementary roles of semantics and prosody in pragmatic interpretation and illustrate how modeling can shed light on the mechanisms underlying sarcastic communication.
Abstract:Sarcasm is a pragmatic phenomenon in which speakers convey meanings that diverge from literal content, relying on an interaction between semantics and prosodic expression. However, how these cues jointly contribute to the recognition of sarcasm remains poorly understood. We propose a computational framework that models sarcasm as the integration of semantic interpretation and prosodic realization. Semantic cues are derived from an LLaMA 3 model fine-tuned to capture discourse-level markers of sarcastic intent, while prosodic cues are extracted through semantically aligned utterances drawn from a database of sarcastic speech, providing prosodic exemplars of sarcastic delivery. Using a speech synthesis testbed, perceptual evaluations show that semantic and prosodic cues enhance perceived sarcasm, with the combined system achieving the best downstream F1 while maintaining high subjective sarcasm ratings. These findings highlight the complementary roles of semantics and prosody in pragmatic interpretation and illustrate how modeling can shed light on the mechanisms underlying sarcastic communication.
Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
arXiv:2510.07096 [cs.CL]
(or arXiv:2510.07096v3 [cs.CL] for this version)
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