Acoustic correlates of speech intelligibility: the usability of the eGeMAPS feature set for atypical speech

Although speech intelligibility has been studied in different fields such as speech pathology, language learning, psycholinguistics, and speech synthesis, it is still unclear which concrete speech features most impact intelligibility. Commonly used subjective measures of speech intelligibility based on labour-intensive human ratings are time-consuming and expensive, so objective procedures based on automatically calculated features are needed. In this paper, we investigate possible correlations between a set of objective features and speech intelligibility. Specifically, we study the usability of acoustic features in the eGeMAPS feature set for predicting phoneme intelligibility by using stepwise linear multiple regression analysis. The results showed that the acoustic features are potentially usable for predicting intelligibility. This finding may help to boost the development of automatic procedures to measure speech intelligibility with the underlying relevant acoustic phonetic characteristics. Our analysis also covers the comparison between two speech types (dysarthric and normal), and between two different types of speech material (isolated words and running text). Finally, we discuss possible avenues for future research on speech intelligibility and implications for clinical practice.