Wake-up-word detection for robots using spatial eigenspace consistency and resonant curve similarity

Jwu-Sheng Hu*, Ming Tang Lee, Ting Chao Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

In this paper, we propose a method to detect the wake-up-word (WUW) using microphone array for human-robot interaction. The consistency of the spatial eigenspaces formed by the speech source at different frequencies and the resonant curve similarity of the WUW are used as the features for detection. These features are processed and detected separately and the result is determined by cascading individual outcome using Bayes risk detector. This proposed method can keep a high recognition rate under very low signal-to-noise ratio (SNR) conditions. In addition, this method can estimate the direction of arrivals of the sound source, and the proposed architecture is easy to expand by adding detectors with other features in the cascaded manner to further improve the recognition rate.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Robotics and Automation, ICRA 2011
Pages3901-3906
Number of pages6
DOIs
StatePublished - 1 Dec 2011
Event2011 IEEE International Conference on Robotics and Automation, ICRA 2011 - Shanghai, China
Duration: 9 May 201113 May 2011

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2011 IEEE International Conference on Robotics and Automation, ICRA 2011
CountryChina
CityShanghai
Period9/05/1113/05/11

Fingerprint Dive into the research topics of 'Wake-up-word detection for robots using spatial eigenspace consistency and resonant curve similarity'. Together they form a unique fingerprint.

Cite this