Enhanced Speech Recognition with Blind Equalization For Robot "WEVER-R2"

Ambient noise caused by room acoustics usually degrades quality and reliability of speech recognition for the service robot application. In this paper, we propose enhancing the robustness of speech recognition for home service robots by combining blind equalization with classification. In particular, the linear predictive code (LPC) is used for feature extraction. The constant modulus algorithm (CMA) is combined with the radial basis function (RBF) neural network to form a robust classifier with automatic equalization capability to reduce the room service effect. Using real speech data collected by WEVER-R2 acoustic robot, it is shown that the proposed method can increase the speech recognition rate from 74.46% to 87.23% for age recognition and from 80.95% to 95.09% for gender recognition.