EgoSpeak: Learning When to Speak for Egocentric Conversational Agents in
  the Wild
Authors: name: Junhyeok Kim, name: Min Soo Kim, name: Jiwan Chung, name: Jungbin Cho, name: Jisoo Kim, name: Sungwoong Kim, name: Gyeongbo Sim, name: Youngjae Yu

Predicting when to initiate speech in real-world environments remains a
fundamental challenge for conversational agents. We introduce EgoSpeak, a novel
framework for real-time speech initiation prediction in egocentric streaming
video. By modeling the conversation from the speaker's first-person viewpoint,
EgoSpeak is tailored for human-like interactions in which a conversational
agent must continuously observe its environment and dynamically decide when to
talk. Our approach bridges the gap between simplified experimental setups and
complex natural conversations by integrating four key capabilities: (1)
first-person perspective, (2) RGB processing, (3) online processing, and (4)
untrimmed video processing. We also present YT-Conversation, a diverse
collection of in-the-wild conversational videos from YouTube, as a resource for
large-scale pretraining. Experiments on EasyCom and Ego4D demonstrate that
EgoSpeak outperforms random and silence-based baselines in real time. Our
results also highlight the importance of multimodal input and context length in
effectively deciding when to speak.