Machine Learning for Parameter Estimation in the MBSTOI Binaural Intelligibility Metric

Abstract

Speech intelligibility metrics are widely used as a replacement for lengthy, in-person intelligibility tests. The short time objective intelligibility (STOI) metric and the more recent modified binaural STOI (MBSTOI), have proven to be reliable in many situations. At the same time, the recent wider accessibility of machine learning (ML) and deep learning (DL) models has lead to the creation of many ML based intelligibility metrics hoping to further improve such metrics. The deep equalisation cancellation MBSTOI model (DEC-MBSTOI) is here presented as a first step toward a hybrid format. The lengthy Equalisation Cancellation (EC) stage of MBSTOI is replaced by a DL model and its performance assessed in terms of a sensitivity analysis performed on the EC stage of MB-STOI. ML is here used to solve an arguably simple problem for accurate metric reproduction and good performance is observed.