Switching to Real-Time Tasks in Multi-Tasking Dialogue

Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 1025–1032 Manchester, August 2008 Switching to Real-Time Tasks in Multi-Tasking Dialogue Fan Yang and Peter A. Heeman Center for Spoken Language Understanding OGI School of Science & Engineering Oregon Health & Science University fly,heeman@cslu.ogi.edu Andrew Kun Electrical and Computer Engineering University of New Hampshire andrew.kun@unh.edu Abstract In this paper we describe an empirical study of human-human multi-tasking dia- logues (MTD), where people perform mul- tiple verbal tasks overlapped in time. We examined how conversants switch from the ongoing task to a real-time task. We found that 1) conversants use discourse markers and prosodic cues to signal task switch- ing, similar to how they signal topic shifts in single-tasking speech; 2) conversants strive to switch tasks at a less disruptive place; and 3) where they cannot, they ex- ert additional effort (even higher pitch) to signal the task switching. Our machine learning experiment also shows that task switching can be reliably recognized using discourse context and normalized pitch. These findings will provide guidelines for building future speech interfaces to sup- port multi-tasking dialogue. 1 Introduction Existing speech interfaces have mostly been used to perform a single task. However, we envision that next-generation speech interfaces will be able to work with the user on multiple tasks at the same time, which is especially useful for real-time tasks. For instance, a driver in a car might use a speech interface to catch up on emails, while occasionally checking upcoming traffic conditions, and receiv- ing navigation instructions. Several speech interfaces that allow multi- tasking dialogues have been built (Lemon et al., 2002; Kun et al., 2004). However, these interfaces c⃝ 2008. Licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported li- cense (http://creativecommons.org/licenses/by-nc-sa/3.0/). Some rights reserved. freely switch between different tasks without much signaling. Thus the user might be confused about which task the interface is talking about. Multi- tasking dialogues, even in the best circumstances, will be difficult for users, as users need to remem- ber the details of each task and be aware of task switching. In order to build a speech interface that supports multi-tasking dialogue, there needs to be a set of conventions for the user and the interface to follow in switching between tasks. To design such a set, we propose to start with conventions that are ac- tually used in human-human conversations, which are natural for users to follow and probably effi- cient in problem-solving. Multi-tasking dialogues, where multiple independent topics overlap with each other in time, regularly arise in human-human conversation: for example, a driver and a navigator in a car might be talking about their summer plans, while occasionally interjecting road directions or conversation about what music to listen to. In order to better understand the human con- ventions on task switching, we have collected the MTD corpus (Heeman et al., 2005), which consists of a set of human-human dialogues where pairs of conversants have multiple overlapping verbal tasks to perform: an ongoing task …

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