On the use of Band Importance Weighting in the Short-Time Objective Intelligibility Measure

Speech intelligibility prediction methods are popular tools within the speech processing community for objective evaluation of speech intelligibility of e.g. enhanced speech. The Short-Time Objective Intelligibility (STOI) measure has become highly used due to its simplicity and high prediction accuracy. In this paper we investigate the use of Band Importance Functions (BIFs) in the STOI measure, i.e. of unequally weighting the contribution of speech information from each frequency band. We do so by fitting BIFs to several datasets of measured intelligibility, and cross evaluating the prediction performance. Our findings indicate that it is possible to improve prediction performance in specific situations. However, it has not been possible to find BIFs which systematically improve prediction performance beyond the data used for fitting. In other words, we find no evidence that the performance of the STOI measure can be improved considerably by extending it with a non-uniform BIF.