Towards an online voice-based gender and internal state detection model

Author

Aly, Amir ; Tapus, Adriana

Author_Institution

Cognitive Robot. Lab./UEI, ENSTA-ParisTech, Paris, France

fYear

2011

fDate

8-11 March 2011

Firstpage

105

Lastpage

106

Abstract

In human-robot interaction, gender and internal state detection play an important role in making the robot reacting in an appropriate manner. This research focuses on the important features to extract from a voice signal in order to construct successful gender and internal state detection systems, and shows the benefits of combining both systems together on the total average recognition score. Moreover, it consists a foundation on an ongoing approach to estimate the human internal state online via unsupervised clustering algorithms.

Keywords

feature extraction; gender issues; human-robot interaction; pattern clustering; speech processing; unsupervised learning; feature extraction; human-robot interaction; online human internal state estimation; online voice-based gender detection model; online voice-based internal state detection model; unsupervised clustering algorithm; voice signal; Clustering algorithms; Databases; Feature extraction; Humans; Robots; Speech; Speech recognition; Experimentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Human-Robot Interaction (HRI), 2011 6th ACM/IEEE International Conference on

Conference_Location

Lausanne

ISSN

2167-2121

Print_ISBN

978-1-4673-4393-0

Electronic_ISBN

2167-2121

Type

conf

Filename

6281246