Laughter Synthesis

Laughter synthesis aims to create realistic and diverse artificial laughter, bridging a gap in expressive virtual agents and interactive art. Current research focuses on developing robust models, including generative adversarial networks (GANs) and diffusion models, trained on large, diverse datasets of laughter recordings often represented using novel phonetic tokenization methods. These advancements are improving the naturalness and emotional range of synthesized laughter, impacting fields like human-computer interaction and artistic expression by enabling more engaging and emotionally nuanced virtual characters and interactive experiences. Challenges remain in accurately capturing the complex interplay between audio, facial expressions, and body language during laughter.