Spoken text difficulty estimation using linguistic features

Abstract

We present an automated method for estimating the difficulty of spoken texts for use in generating items that assess non-native learners' listening proficiency. We collected information on the perceived difficulty of listening to various English monologue speech samples using a Likert-scale questionnaire distributed to 15 non-native English learners. We averaged the overall rating provided by three nonnative learners at different proficiency levels into an overall score of listenability. We then trained a multiple linear regression model with the listenability score as the dependent variable and features from both natural language and speech processing as the independent variables. Our method demonstrated a correlation of 0.76 with the listenability score, comparable to the agreement between the nonnative learners' ratings and the listenability score.