Beyond accuracy: Multimodal modeling of structured speaking skill indices in young adolescents - Candy Olivia Mawalim; Chee Wee Leong; Guy Sivan; Hung-Hsuan Huang; Shogo Okada (Computers and Education: Artificial Intelligence)
This study introduces a novel method for explainable speaking skill assessment that utilizes a unique dataset featuring video recordings of conversational interviews for high-stakes outcomes (i.e., admission to high schools and universities). Unlike traditional automated speaking assessments that prioritize accuracy at the expense of interpretability, our approach employs a new multimodal dataset that integrates acoustic and linguistic features, visual cues, turn-taking patterns, and expert-derived scores quantifying various speaking skill aspects observed during interviews with young adolescents. This dataset is distinguished by its open-ended question format, which allows for varied responses from interviewees, providing a rich basis for analysis. The experimental results demonstrate that fusing interpretable features, including prosody, action units, and turn-taking, significantly enhances the accuracy of spoken English skill prediction, achieving an overall accuracy of 83% when a machine learning model based on the light gradient boosting algorithm is used. Furthermore, this research underscores the significant influence of external factors, such as interviewer behavior and the interview setting, particularly on the coherence aspect of spoken English proficiency. This focus on an innovative dataset and interpretable assessment tools offers a more nuanced understanding of speaking skills in high-stakes contexts than that offered by previous studies.