Paper Info
Title
Estimating a user’s internal state before the first input utterance
Abstract
This paper describes a method for estimating the internal state of a user of a spoken dialog system before his/her first input utterance. When actually using a dialog-based system, the user is often perplexed by the prompt. A typical system provides more detailed information to a user who is taking time to make an input utterance, but such assistance is nuisance if the user is merely considering how to answer the prompt. To respond appropriately, the spoken dialog system should be able to consider the user's internal state before the user's input. Conventional studies on user modeling have focused on the linguistic information of the utterance for estimating the user's internal state, but this approach cannot estimate the user's state until the end of the user's first utterance. Therefore, we focused on the user's nonverbal output such as fillers, silence, or head-moving until the beginning of the input utterance. The experimental data was collected on a Wizard of Oz basis, and the labels were decided by five evaluators. Finally, we conducted a discrimination experiment with the trained user model using combined features. As a three-class discrimination result, we obtained about 85% accuracy in an open test.
Year
DOI
Venue
2012
10.1155/2012/865362
Adv. Human-Computer Interaction
Keywords
Field
DocType
detailed information,dialog-based system,user modeling,dialog system,input utterance,typical system,discrimination experiment,internal state,trained user model,linguistic information
Dialog box,Rule-based machine translation,Spoken dialog,Computer science,Utterance,Nonverbal communication,Speech recognition,Artificial intelligence,User modeling,Natural language processing,Wizard of oz
Journal
Volume
ISSN
Citations 
2012,
1687-5893
3
PageRank 
References 
Authors
0.40
31
2
Name
Order
Citations
PageRank
Yuya Chiba186.96
Akinori Ito227262.32