Investigating the potential of NLP-driven linguistic and acoustic features for predicting human scores of children’s oral language proficiency

ABSTRACT Children’s oral language proficiency (OLP) is integral for developing literacy skills. Storytelling or retelling is often used by parents and educators to elicit children’s OLP, yet it is less commonly used for assessment purposes. Leveraged by natural language processing and machine learning, this study examined the extent to which computational linguistic and acoustic indices predict human ratings of children’s (n=184 aged 9 to 11) OLP using two story retell stimuli presented in written and aural forms. Human raters scored children’s OLP on five oral proficiency criteria: vocabulary, grammar, idea development, task-fulfilment, and speech delivery, using a 4-point scale, and linguistic and acoustic features were used to predict each criterion. Results showed the efficacy of automated indices to predict human scores of children’s OLP. This study calls for attention to discrepancies in human and machine speech delivery scores and stimulus effects on story retelling performance among children of different language backgrounds.