Differential Impacts of Monologue and Conversation on Speech Emotion Recognition

Abstract

The advancement of Speech Emotion Recognition (SER) is significantly dependent on the quality of emotional speech corpora used for model training. Researchers in the field of SER have developed various corpora by adjusting design parameters to enhance the reliability of the training source. For this study, we focus on exploring communication modes of collection, specifically analyzing spontaneous emotional speech patterns gathered during conversation or monologue. While conversations are acknowledged as effective for eliciting authentic emotional expressions, systematic analyses are necessary to confirm their reliability as a better source of emotional speech data. We investigate this research question from perceptual differences and acoustic variability present in both emotional speeches. Our analyses on multi-lingual corpora show that, first, raters exhibit higher consistency for conversation recordings when evaluating categorical emotions, and second, perceptions and acoustic patterns observed in conversational samples align more closely with expected trends discussed in relevant emotion literature. We further examine the impact of these differences on SER modeling, which shows that we can train a more robust and stable SER model by using conversation data. This work provides comprehensive evidence suggesting that conversation may offer a better source compared to monologue for developing an SER model.