Speech recognition using circular microphone array is addressed in this paper. Eight microphones are located around the service robot to form a 2D microphone array. To enhance the speech quality, a novel adaptive beamformer composed of a delay-and-sum beamformer, adaptive blocking filters (ABFs) and adaptive cancelling filters (ACFs) is proposed. While the adaptive generalized sidelobe canceller (AGSC) connects the ABF and the ACF in feedforward, the proposed adaptive beamformer has them in feedback. The advantages of the proposed structure are the robustness to the steering vector errors and cross-talks and the reduced number of filter taps that gives the same speech quality compared to the AGSC with a larger number of filter taps. The experimental results show that the proposed structure is superior to the AGSC in objective and subjective evaluations. Speech recognition result shows that the proposed robust adaptive beamformer guarantees the recognition performance even in a low SNR and highly reverberant environment.

The problem of trajectory tracking control of a three dimensional (3D) model of the human upper torso and head is considered. The torso and the head are modeled as two rigid bodies connected at one point, and the Newton-Euler method is used to derive the nonlinear differential equations that govern the motion of the system. The two-link system is driven by six pairs of muscle like actuators that possess physiologically inspired alpha like and gamma like inputs, and spindle like and Golgi tendon organ like outputs. These outputs are utilized as reflex feedback for stability and stiffness control, in a long loop feedback for the purpose of estimating the state of the system (somesthesis), and as part of the input to the controller. Ideal delays of different duration are included in the feedforward and feedback paths of the system to emulate such delays encountered in physiological systems. Dynamical neural networks are trained to learn effective control of the desired maneuvers of the system. The feasibility of the controller is demonstrated by computer simulation of the successful execution of the desired maneuvers. This work demonstrates the capabilities of neural circuits in controlling highly nonlinear systems with multidelays in their feedforward and feedback paths. The ultimate long range goal of this research is toward understanding the working of the central nervous system in controlling movement. It is an interdisciplinary effort relying on mechanics, biomechanics, neuroscience, system theory, physiology and anatomy, and its short range relevance to rehabilitation must be noted.