Internal noise suppression for speech recognition by small robots

摘要

Speech recognition by a small robot is difficult because the robot makes noise itself. In this paper, two new methods are proposed that suppresses internal noise of the small robots. These methods are based on spectral subtraction (SS). The difference of the proposed methods from the original SS is that the proposed methods use the estimated noise spectrum dependent on the motion of the robot. One method, called MDSS, prepares the noise spectrums for all motions. Another method, called NPSS, predicts the noise spectrum from angular velocities of all joints of the robot using a neural network. From the results of the comparison between the original SS and the proposed methods, the proposed methods outperformed the conventional SS. The NPSS worked well even when the noise of the motion was unstable, while the MDSS method gave good result when the noise in one motion was stable.