SarcasticSpeech: Speech Synthesis for Sarcasm in Low-Resource Scenarios
This paper tackles the challenge of synthesizing sarcastic speech, a vital yet underexplored component of expressive speech synthesis. Sarcasm, characterized by a mismatch between literal meaning and intended message, relies heavily on prosodic cues that are difficult to model, especially with limited data resources.
Our research represents one of the first attempts to apply transfer learning to sarcastic speech synthesis. By leveraging a pre-trained model and fine-tuning it with a dataset that includes various speech styles alongside sarcastic samples, we demonstrate that it’s possible to generate speech with some sarcastic qualities despite data constraints.
The study identifies several key challenges in this domain:
- The scarcity of dedicated sarcastic speech datasets
- The complex, context-dependent nature of sarcastic prosody
- The need to balance expressiveness with naturalism in synthesized speech
While our current results show moderate success with some artifactual elements in the synthesized output, this work establishes an important baseline and direction for future research. We propose that multimodal approaches incorporating textual, acoustic, and potentially visual cues might lead to more convincing sarcastic speech synthesis in future iterations.
This research has applications in enhancing human-computer interaction, creating more engaging virtual assistants, and developing tools for entertainment and educational contexts where expressive speech is valuable.
