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October 2010

An easily-configurable robot audition system using Histogram-based Recursive Level Estimation

  • H. Nakajima, G. Ince, K. Nakadai, Y. Hasegawa,
  • in Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010),
  • IEEE,
  • 2010,
  • pp. 958-963,
  • Conference paper

This paper presents an easily-configurable robot audition system using the Histogram-based Recursive Level Estimation (HRLE) method. In order to achieve natural humanrobot 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 subtractionbased 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 realtime. 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.

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