Interruption Timing Prediction via Prosodic Task Boundary Model for Human-Machine Teaming

  • Nia PetersEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)


Human-machine teaming aims to meld the human cognitive strengths with the unique capabilities of smart machines to create intelligent teams adaptive to rapidly changing circumstances. One major problem within human-machine teaming is a lack of communication skills on the part of the machine such as the inability to know when to communicate information to or interrupt human teammates. To address this issue, an intelligent interruption system that monitors the speech within human-machine teaming interactions and predicts when to interrupt based on where human teammates are within the primary task is proposed. The intelligent interruption system leverages the raw audio within a simulated human-machine teaming interaction, extracts prosodic information, and predicts task boundaries as candidate interruption timings. Various machine learning techniques are evaluated as a prosody-only task boundary model and their task boundary detection performance is compared. The prosody-only task boundary model is implemented in real-time and the system latency and task boundary detection performance is evaluated. The final results indicate that although prosodic information processes with a low latency making it tractable in real-time, the prosody-only task boundary model performance is degraded in robust dialogues of human-machine teaming interactions.


Human machine teaming Intelligent interruption Speech prosody Collaborative communication 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Air Force Research Laboratory, Wright Patterson Air Force BaseDaytonUSA

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