Abstract
In this paper, we focus on performance comparison of gender and age group recognition to perform robot’s application services for Human-Robot Interaction (HRI). HRI is a core technology that can naturally interact between human and robot. Among various HRI components, we concentrate audio-based techniques such as gender and age group recognition from multichannel microphones and sound board equipped with robots. For comparative purposes, we perform the performance comparison of Mel-Frequency Cepstral Coefficients (MFCC) and Linear Prediction Coding Coefficients (LPCC) in the feature extraction step, Support Vector Machine (SVM) and C4.5 Decision Tree (DT) in the classification step. Finally, we deal with the usefulness of gender and age group recognition for human-robot interaction in home service robot environments.
Highlights
Conventional industrial robots perform jobs and simple tasks by following pre-programmed instructions for humans in factories
Among various audio-based HumanRobot Interaction (HRI) components, we focus on gender and age group recognition
We have performed the comparative analysis for gender and age group classification of audio-based HRI components
Summary
Conventional industrial robots perform jobs and simple tasks by following pre-programmed instructions for humans in factories. The main goal of the intelligent service robot is to adapt to the necessities of life as accessibility to human life increases. There has been a renewal of interest in HumanRobot Interaction (HRI) for intelligent service robots [1,2]. This is different from HCI (Human-Computer Interaction) in that robots have an autonomous movement, a bidirectional feature of interaction, and diversity of control level. Audio-based HRI technology includes speech recognition, speaker recognition [3][4], sound source localization [5], sound source separation, speech emotional recognition, speech enhancement, gender and age group recognition. Among various audio-based HRI components, we focus on gender and age group recognition. We perform the performance comparison in the step of feature extraction (MFCC, LPCC) and classification (SVM, C4.5) for gender and age group classification
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