## 2 Proposed System
### Selection of OIM  
In principle, any OIM may serve as the intelligibility meter in Figure 1. However, previous studies (e.g., [24; 34]) have shown that some OIMs do not work well across the broad range of maskers found in broadcast audio. The background may vary over time from music to crowd babble sound, from general soundscapes to competing talkers. Building a large database of statistics of various background sounds to rectify each model output is therefore impractical. To solve this problem, [PERSON] proposed a metric--distortion-weighted glimpse proportion (DWGP, [24])--based on a notion that both masked audibility and disturbance on speech modulation resulting from the masking effect of the noise masker, should account for the reduction of speech intelligibility in noise. The former aspect is quantified in each analytical frequency band as the percentage of the time-frequency regions on the speech signal with a speech-to-noise ratio above a pre-defined threshold (e.g., 3 dB); the latter effect is calculated as the distortion factor for each frequency, using cross-correlation between the envelopes of the clean and the noise-corrupted speech signals. The distortion factor is then used to weight the masked audibility in each band. The final predictive score is the sum of the masked audibilities across all the frequencies after further being weighted by the band importance function [20], which determines the contribution of each frequency to the overall intelligibility. In assessment, the DWGP metric has demonstrated a good match to subjective data when treating each noise separately and across a wide range of background noise, with squared Pearson correlation coefficients \(r^{2}>0.71\)[24].  
In 2015, [PERSON] et al. further extended the DWGP metric to binaural DWGP (BiDWGP), thus affording a more ecologically valid application of the metric [9]. See [28] for implementation details of BiDWGP. Two prominent aspects of binaural listening are modelled in this extension: the better-ear effect due to the head-shadowing and binaural unmasking. BiDWGP can predict intelligibility either directly from the binaural recordings or from monophonic recordings of each sound source along with their locations. Predictions made using the two input forms are almost identical in anechoic conditions [28]. As an extension to DWGP, when BiDWGP is used for monaural situations it operates the same to DWGP. Therefore, BiDWGP is able to replace its monaural counterpart in any cases.  
The evaluations in both anechoic [28] and reverberant [34] conditions have suggested that the BiDWGP outperformed the standard metrics such as the SII (\(r^{2}\geq 0.48\)) and the STI (\(r^{2}\geq 0.62\)) in many conditions, and was able to provide reliable intelligibility estimations (\(r^{2}\geq 0.77\)). Figure 2 furthers compares the listeners' word recognition rates at sentence level from three previous studies [9; 34; 35] against BiDWGP predictions in both anechoic and reverberant conditions, in which stimuli were  
Figure 1: Schematic of a rendering system with intelligibility control. The current study focuses on the section in shadow.  
presented monaurally or binaurally. A strong linear relationship between the subjective data and BiDWGP prediction is evident with \(r^{2}=0.81\) across all 190 data points/conditions. Providing its robust predictive power, the BiDWGP metric was hence chosen as the OIM for intelligibility metering in the work presented here.