# Modeling Sarcastic Speech: Semantic and Prosodic Cues in a Speech Synthesis Framework

arXiv: https://arxiv.org/abs/2510.07096 · HTML: https://mlsanity.com/paper/2510.07096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://mlsanity.com/paper/1906.01815/) (2019)
- [Hi-Fi Multi-Speaker English TTS Dataset](https://mlsanity.com/paper/2104.01497/) (2021)
- [Controllable Emotion Transfer For End-to-End Speech Synthesis](https://mlsanity.com/paper/2011.08679/) (2020)
- [A Multimodal Corpus for Emotion Recognition in Sarcasm](https://mlsanity.com/paper/2206.02119/) (2022)
- [Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder](https://mlsanity.com/paper/1804.02135/) (2018)
- [Retrieval Augmented Generation in Prompt-based Text-to-Speech Synthesis with Context-Aware Contrastive Language-Audio Pretraining](https://mlsanity.com/paper/2406.03714/) (2024)
- [A Functional Trade-off between Prosodic and Semantic Cues in Conveying Sarcasm](https://mlsanity.com/paper/2408.14892/) (2024)
- [Get Large Language Models Ready to Speak: A Late-fusion Approach for Speech Generation](https://mlsanity.com/paper/2410.20336/) (2024)
- [AMuSeD: An Attentive Deep Neural Network for Multimodal Sarcasm Detection Incorporating Bi-modal Data Augmentation](https://mlsanity.com/paper/2412.10103/) (2024)
- [Integrating Feedback Loss from Bi-modal Sarcasm Detector for Sarcastic Speech Synthesis](https://mlsanity.com/paper/2508.13028/) (2025)
- [Evaluating Multimodal Large Language Models on Spoken Sarcasm Understanding](https://mlsanity.com/paper/2509.15476/) (2025)
- [Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness](https://mlsanity.com/paper/2404.06714/) (2024)
- [AutoStyle-TTS: Retrieval-Augmented Generation based Automatic Style Matching Text-to-Speech Synthesis](https://mlsanity.com/paper/2504.10309/) (2025)

## Built on by (most cited first, 1 of 1)

- [CLASH: Counterfactual Auditing of Lexical and Prosodic Reliance in Spoken Sarcasm Detection](https://mlsanity.com/paper/2609.16582/) (2026)

## Abstract (arXiv)

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.