Response Obligation Estimation That Considers Users' Repetitive Utterances using Knowledge-Guided Random Forest

Kotaro Funakoshi*, Ryota Yamagami, Shigeki Sugano, Mikio Nakano

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Response obligation is whether a spoken dialogue system should react to an input sound. This paper focuses on the false negative errors in response obligation estimation (ROE) that are displayed as the system's neglect of its users. When the users repeat after the system ignores their speech, ROE will likely fail again because the repeated input is similar to the previous input. Therefore, we propose an improved ROE method that considers users' repetitions. First, we show that a simple concatenation of ROE and repetition features is better than two other integration architectures. Then, we propose a modified random forest algorithm that incorporates human domain knowledge. The effectiveness is demonstrated with simulated repetitions as a 7.6-point gain from the baseline.

Original languageEnglish
Title of host publication2019 IEEE-RAS 19th International Conference on Humanoid Robots, Humanoids 2019
PublisherIEEE Computer Society
Pages99-105
Number of pages7
ISBN (Electronic)9781538676301
DOIs
Publication statusPublished - 2019 Oct
Event19th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2019 - Toronto, Canada
Duration: 2019 Oct 152019 Oct 17

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
Volume2019-October
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Conference

Conference19th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2019
Country/TerritoryCanada
CityToronto
Period19/10/1519/10/17

Keywords

  • human-robot interaction
  • knowledge-guided machine learning
  • multimodal dialogue

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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