Using Probabilistic Feature Matching to Understand Spoken Descriptions

Authors:
Ingrid Zukerman;Enes Makalic;Michael Niemann
Affiliations:
Faculty of Information Technology, Monash University, Clayton, Australia 3800;Faculty of Information Technology, Monash University, Clayton, Australia 3800;Faculty of Information Technology, Monash University, Clayton, Australia 3800
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

Citing 8
Cited 2

Conceptual structures: information processing in mind and machine

Conceptual structures: information processing in mind and machine
Assessing agreement on classification tasks: the kappa statistic

Computational Linguistics
Object Recognition Supported by User Interaction for Service Robots

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Empirical Evaluation of Dissimilarity Measures for Color and Texture

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Generating referring expressions in open domains

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Attention driven reference resolution in multimodal contexts

Artificial Intelligence Review
A Probabilistic Approach to the Interpretation of Spoken Utterances

PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
WordNet: similarity - measuring the relatedness of concepts

AAAI'04 Proceedings of the 19th national conference on Artifical intelligence

A Probabilistic Approach to the Interpretation of Spoken Utterances

PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
A Probabilistic Model for Understanding Composite Spoken Descriptions

PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a probabilistic reference disambiguation mechanism developed for a spoken dialogue system mounted on an autonomous robotic agent. Our mechanism performs probabilistic comparisons between features specified in referring expressions (e.g. size and colour) and features of objects in the domain. The results of these comparisons are combined using a function weighted on the basis of the specified features. Our evaluation shows high reference resolution accuracy across a range of spoken referring expressions.