This paper presents a single-chip speech dialogue module and its evaluation on a personal robot. This module is implemented on an application processor that was developed primarily for mobile phones to provide a compact size, low power-consumption, and low cost. It performs speech recognition with preprocessing functions such as direction-of-arrival (DOA) estimation, noise cancellation, beamforming with an array of microphones, and echo cancellation. Text-to-speech (TTS) conversion is also equipped with. Evaluation results obtained on a new personal robot, PaPeRo-mini, which is a scale-down version of PaPeRo, demonstrate an 85% correct rate in DOA estimation, and as much as 54% and 30% higher speech recognition rates in noisy environments and during robot utterances, respectively. These results are shown to be comparable to those obtained by PaPeRo.

The usual method for position estimation of a wheeled mobile robot is odometry. However, it has the problem of gradual error accumulation when the robot moves. To solve this problem, we place several reflectors sparsely in the robot's work-space as landmarks, so that the robot can correct its estimated position when detecting the landmark. The new estimated position of the robot is calculated by the maximum likelihood estimation (MLE) by both the position information estimated by odometry and the reflector detecting sensor system (ReDS) using the laser beam. ReDS, which we developed, can detect the reflector at most 3 meters away from the robot in the indoor environment. Practical experiments show that the estimated position obtained by this system is precise enough to be useful.