Microscopic and Blind Prediction of Speech Intelligibility: Theory and Practice

Modeling the effect of linguistic predictability on speech intelligibility prediction

Many existing speech intelligibility prediction (SIP) algorithms can only account for acoustic factors affecting speech intelligibility and cannot predict intelligibility across corpora with different linguistic predictability. To address this, a linguistic component was added to five existing SIP algorithms by estimating linguistic corpus predictability using a pre-trained language model. The results showed improved SIP performance in terms of correlation and prediction error over a mixture of four datasets, each with a different English open-set corpus.

ASR-based speech intelligibility prediction: A review

Various types of methods and approaches are available to predict the intelligibility of speech signals, but many of these still suffer from two major problems: first, their required prior knowledge, which itself could limit the applicability and lower the objectivity of the method, and second, a low generalization capacity, e.g. across noise types, degradation conditions, and speech material. Automatic speech recognition (ASR) has been suggested as a machine-learning-based component of speech intelligibility prediction (SIP), aiming to ameliorate the shortcomings of other SIP methods. Since their first introduction, ASR-based SIP approaches have been developing at an increasingly rapid pace, were deployed in a range of contexts, and have shown promising performance in many scenarios. Our article provides an overview of this body of research. The main differences between competing methods are highlighted and their benefits are explained next to their limitations. We conclude with an outlook on future work and new related directions.