Measuring speech intelligibility is important for evaluating speech enhancement algorithms, but performing these measurements physically is a time-consuming and costly process. Various metrics have therefore been developed that can predict binaural intelligibility based on clean target and noise (or noisy) input sound signals. One such metric, based on the equalisation cancellation (EC) model, is the binaural speech intelligibility model (BSIM). The way that this metric is formulated math ematically allows a reformulation that is more compact and efficient. In this paper, the EC process is interpreted as an ‘internal’ beamformer, casting the model into a linear algebra framework. This framework makes the BSIM mathematically and computationally more compact and easier to be combined with signal processing or machine learning strategies. Simulations of the original and proposed implementations of the BSIM show that the internal beamformer framework results in similar intelligibility predictions with a typical 5 to 10 times reduction in simulation time. By analysing the modified binaural short-time objective intelligibility (MBSTOI) metric in this framework, it is shown that the presented concepts can be extended to other binaural intelligibility metrics as well.

Hearing impairment is a prevalent problem with daily challenges like impaired speech intelligibility and sound localisation. One of the shortcomings of spatial filtering in hearing aids is that speech intelligibility is often not optimised directly, meaning that different auditory processes contributing to intelligibility are often not considered. One example is the perceptual phenomenon known as spatial release from masking (SRM). This paper develops a signal model that explicitly considers SRM in the beamforming design, achieved by transforming the binaural intelligibility prediction model (BSIM) into a signal processing framework. The resulting extended signal model is used to analyse the performance of reference beamformers and design a novel beamformer that more closely considers how the auditory system perceives binaural sound. It can be shown that the binaural minimum variance distortionless response (BMVDR) beamformer is also an optimal solution for the extended, perceived model, suggesting that SRM does not play a significant role in intelligibility enhancement after optimal beamforming. However, the optimal beamformer is no longer unique in the extended signal model. The additional secondary degrees of freedom can be used to preserve binaural cues of interfering sources while still achieving the same perceived performance of the BMVDR beamformer, though with a possible high sensitivity to intelligibility model mismatch errors.