  Beamforming techniques are popular in speech-related applications due to
their effective spatial filtering capabilities. Nonetheless, conventional
beamforming techniques generally depend heavily on either the target's
direction-of-arrival (DOA), relative transfer function (RTF) or covariance
matrix. This paper presents a new approach, the intelligibility-aware
null-steering (IANS) beamforming framework, which uses the STOI-Net
intelligibility prediction model to improve speech intelligibility without
prior knowledge of the speech signal parameters mentioned earlier. The IANS
framework combines a null-steering beamformer (NSBF) to generate a set of
beamformed outputs, and STOI-Net, to determine the optimal result. Experimental
results indicate that IANS can produce intelligibility-enhanced signals using a
small dual-microphone array. The results are comparable to those obtained by
null-steering beamformers with given knowledge of DOAs.
