Deconvolved Conventional Beamforming and Adaptive Cubature Kalman Filter Based Distant Speech Perception System

Abstract

A spatial-temporal processing framework integrated of speech enhancement and speech tracking is proposed in this paper for distant speech perception. First, weak speech signals are enhanced by the deconvolved conventional beamforming (DCBF) with a microphone array. By virtue of the narrow beamwidth and low sidelobes of the DCBF, the competing sources can be effectively suppressed without introducing extra speech distortion. Second, with the accurate bearing provided by the DCBF, the Cubature Kalman filter can be utilized to track the speech source of interest. By introducing a scaling factor in the current statistical motion model, a new tracking algorithm is proposed which is suitable for both maneuvering and nonmaneuvering speech sources. The introduced scaling factor can be adaptively adjusted to improve the tracking performance of the proposed algorithm for different motion models. Numerical results show that the proposed algorithm can provide better tracking performance than the conventional one. In particular, the tracking root mean square error can be reduced by half for some cases.