  In this work, we propose a robust Head-Related Transfer Function (HRTF)-based
polynomial beamformer design which accounts for the influence of a humanoid
robot's head on the sound field. In addition, it allows for a flexible steering
of our previously proposed robust HRTF-based beamformer design. We evaluate the
HRTF-based polynomial beamformer design and compare it to the original
HRTF-based beamformer design by means of signal-independent measures as well as
word error rates of an off-the-shelf speech recognition system. Our results
confirm the effectiveness of the polynomial beamformer design, which makes it a
promising approach to robust beamforming for robot audition.
