Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics (TIC-TALK): Pipeline and Database for the Multimodal Study of Comedic Timing

2026-06-27 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

TIC-TALK is a novel multimodal resource and pipeline designed for studying comedic timing in stand-up performances. It comprises over 5,400 temporally aligned topic segments extracted from 90 professionally filmed stand-up comedy specials recorded between 2015 and 2024. The resource integrates data streams for language, gesture, and audience response. Its pipeline utilizes BERTopic for 60-second thematic segmentation with dense sentence embeddings, Whisper-AT for 0.8-second laughter detection, a fine-tuned YOLOv8-cls for shot classification, and YOLOv8s-pose for 1 fps raw 17-joint skeletal keypoint extraction. This enables computation of continuous kinematic signals like arm spread, kinetic energy, and trunk lean. A concrete use case revealed that kinetic energy negatively predicts audience laughter rate (r=−0.75, N=24), supporting a stillness-before-punchline pattern. Additionally, personal content generated more laughter than geopolitical themes, and close-up shot proportion positively correlated with laughter (r=+0.28).

Key takeaway

For research scientists developing AI models for humor generation or performance analysis, you should consider integrating multimodal data streams beyond just verbal content. The TIC-TALK findings suggest that analyzing performer kinetics, such as kinetic energy and trunk lean, alongside audience response and camera work, provides crucial insights into comedic timing. You can utilize these insights to build more nuanced models that account for embodied presence and audience feedback, potentially improving the naturalness and effectiveness of AI-generated comedic performances.

Key insights

Multimodal analysis of stand-up comedy reveals specific correlations between performer kinetics, content, and audience laughter.

Principles

• Comedic timing involves multimodal cues beyond verbal content.
• Stillness often precedes punchlines for audience engagement.
• Personal themes may elicit more laughter than abstract topics.

Method

The TIC-TALK pipeline segments comedy specials thematically using BERTopic, detects laughter with Whisper-AT, classifies shots via YOLOv8-cls, and extracts 17-joint skeletal keypoints at 1 fps using YOLOv8s-pose to derive kinematic signals.

In practice

• Kinetic energy negatively correlates with audience laughter.
• Personal content elicits more laughter than geopolitical themes.
• Close-up shot proportion positively correlates with laughter.

Topics

• Stand-up Comedy
• Multimodal Data
• Comedic Timing
• Laughter Detection
• Kinesics Analysis
• YOLOv8
• BERTopic

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.