Automatic Prediction of Child Speech Fluency with Game-Based Data from German Preschoolers

Abstract

This paper introduces an approach to automatically predict the speech fluency of preschool children as part of Language Proficiency Assessments. We use spontaneous speech data from children with German as native and second language aged 4–6 years, collected via a game–based elicitation method. The recordings were mainly annotated manually on various fluency-related phenomena. The resulting feature values were compared to human fluency ratings of the same data. The human ratings and the fluency-related acoustic features were used to build Cumulative Link Mixed Models (CLMMs) with and without splines to test their ability to predict the human ratings with multiple metrics (Spearman’s ρ, MAE, quadratic weighted κ). Results show that a parsimonious linear model already reaches near-human agreement (quadratic weighted kappa κ = 0.65) and that incorporating non-linear spline effects does not improve predictive accuracy. These findings suggest that relatively simple CLMMs can substitute additional human raters in fine-grained fluency assessment of preschool children, which is a task that is already challenging for trained listeners.

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