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One of the key communicative competencies is the ability to maintain fluencyin monologic speech and the ability to produce sophisticated language to arguea position convincingly. In this paper we aim to predict TED talk-styleaffective ratings in a crowdsourced dataset of argumentative speech consistingof 7 hours of speech from 110 individuals. The speech samples were elicitedthrough task prompts relating to three debating topics. The samples received atotal of 2211 ratings from 737 human raters pertaining to 14 affectivecategories. We present an effective approach to the classification task ofpredicting these categories through fine-tuning a model pre-trained on a largedataset of TED talks public speeches. We use a combination of fluency featuresderived from a state-of-the-art automatic speech recognition system and a largeset of human-interpretable linguistic features obtained from an automatic textanalysis system. Classification accuracy was greater than 60% for all 14 ratingcategories, with a peak performance of 72% for the rating category'informative'. In a secondary experiment, we determined the relative importanceof features from different groups using SP-LIME.

Prediction of Listener Perception of Argumentative Speech in a Crowdsourced Dataset Using (Psycho-)Linguistic and Fluency Features