An easily-configurable robot audition system using histogram-based recursive level estimation

Hirofumi Nakajima*, Gökhan Ince, Kazuhiro Nakadai, Yuji Hasegawa

*Corresponding author for this work

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

22 Citations (Scopus)

Abstract

This paper presents an easily-configurable robot audition system using the Histogram-based Recursive Level Estimation (HRLE) method. In order to achieve natural human-robot interaction, a robot should recognize human speeches even if there are some noises and reverberations. Since the precision of automatic speech recognizers (ASR) have been degraded by such interference, many systems applying speech enhancement processes have been reported. However, performance of most reported systems suffer from acoustical environmental changes. For example, an enhancement process optimized for steady-state noise, such as fan noise, yields low performance when the process is used for non-steady-state noises, such as background music. The primary reason is mismatches of parameters because the appropriate parameters change according to the acoustical environments. To solve this problem, we propose a robot audition system that optimizes parameters adaptively and automatically. Our system applies linear and non-linear enhancement sub-processes. For the linear sub-process, we used Geometric Source Separation with the Adaptive Step-size method (GSS-AS). This adjusts the parameters adaptively and does not have any manual parameters. For the non-linear sub-process, we applied a spectral subtraction-based enhancement method with the HRLE method that is newly introduced in this paper. Since HRLE controls the threshold level parameter implicitly based on the statistical characteristics of noise and speech levels, our system has high robustness against acoustical environmental changes. For robot audition systems, all processes should be perfomed in real-time. We also propose implementation techniques to make HRLE run in real-time and show the effectiveness. We evaluate performance of our system and compare it to conventional systems based on the Minima Controlled Recursive Average (MCRA) method and Minimum Mean Square Error (MMSE) method. The experimental results show that our system achieves better performance than the conventional systems.

Original languageEnglish
Title of host publicationIEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings
Pages958-963
Number of pages6
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Taipei, Taiwan, Province of China
Duration: 18 Oct 201022 Oct 2010

Publication series

NameIEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings

Conference

Conference23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010
Country/TerritoryTaiwan, Province of China
CityTaipei
Period18/10/1022/10/10

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