A joint acoustic and phonological approach to speech intelligibility assessment

While current models of speech intelligibility rely on intricate acoustic analyses of speech attributes, they are limited by the lack of any linguistic information; hence failing to capture natural variability of speech sounds and confining their applicability to average intelligibility assessments. Another important limitation is that the existing models rely on the use of reference clean speech templates (or average profiles). In this work, we propose a novel approach to speech intelligibility by combining a biologically-inspired acoustic analysis of peripheral and cortical processing with phonological statistical models of speech using a hybrid GMM-SVM system. The model results in a novel scheme for speech intelligibility assessment without the use of reference clean speech templates, and the model predictions strongly correlate with scores obtained from human listeners under a variety of realistic listening environments. We further show that the proposed model enables local level tracking of intelligibility and also generalizes well to multiple speech corpora.