This work investigates linguistically motivated features for automatically scoring a spoken picture-based narration task. Specifically, we build scoring models with features for story development, language use and task relevance of the response. Results show that combinations of these features outperform a baseline system that uses state of the art speech-based features, and that best results are obtained by combining the linguistic and speech features.
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