Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

A user modeling-based performance analysis of a wizarded uncertainty-adaptive dialogue system corpus

Forbes-Riley, K and Litman, D (2009) A user modeling-based performance analysis of a wizarded uncertainty-adaptive dialogue system corpus. In: UNSPECIFIED.

[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)

Abstract

Motivated by prior spoken dialogue system research in user modeling, we analyze interactions between performance and user class in a dataset previously collected with two wizarded spoken dialogue tutoring systems that adapt to user uncertainty. We focus on user classes defined by expertise level and gender, and on both objective (learning) and subjective (user satisfaction) performance metrics. We find that lower expertise users learn best from one adaptive system but prefer the other, while higher expertise users learned more from one adaptive system but didn't prefer either. Female users both learn best from and prefer the same adaptive system, while males preferred one adaptive system but didn't learn more from either. Our results yield an empirical basis for future investigations into whether adaptive system performance can improve by adapting to user uncertainty differently based on user class. Copyright © 2009 ISCA.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Forbes-Riley, K
Litman, Ddlitman@pitt.eduDLITMAN
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 26 November 2009
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Page Range: 2467 - 2470
Event Type: Conference
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
Date Deposited: 21 Nov 2014 21:02
Last Modified: 02 Feb 2019 15:59
URI: http://d-scholarship.pitt.edu/id/eprint/22969

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item