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Beamforming techniques are popular in speech-related applications due totheir effective spatial filtering capabilities. Nonetheless, conventionalbeamforming techniques generally depend heavily on either the target'sdirection-of-arrival (DOA), relative transfer function (RTF) or covariancematrix. This paper presents a new approach, the intelligibility-awarenull-steering (IANS) beamforming framework, which uses the STOI-Netintelligibility prediction model to improve speech intelligibility withoutprior knowledge of the speech signal parameters mentioned earlier. The IANSframework combines a null-steering beamformer (NSBF) to generate a set ofbeamformed outputs, and STOI-Net, to determine the optimal result. Experimentalresults indicate that IANS can produce intelligibility-enhanced signals using asmall dual-microphone array. The results are comparable to those obtained bynull-steering beamformers with given knowledge of DOAs.

IANS: Intelligibility-aware Null-steering Beamforming for Dual-Microphone Arrays