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

TL;DR

This study predicts TED talk-style affective ratings in argumentative speech using a combination of linguistic and fluency features, achieving over 60% accuracy across 14 categories with a fine-tuned pre-trained model.

Contribution

It introduces an effective method combining fluency and linguistic features for predicting affective ratings in argumentative speech datasets.

Findings

Classification accuracy exceeded 60% for all categories

Peak accuracy of 72% for 'informative' ratings

Feature importance analysis identified key contributing features

Abstract

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