Bridging Speech Emotion Recognition and Personality: Dataset and Temporal Interaction Condition Network

TL;DR

This paper introduces a new dataset with both emotion and personality annotations, and a novel neural network model that leverages personality traits to improve speech emotion recognition accuracy.

Contribution

It provides the first dataset combining emotion and personality annotations and proposes a TICN model that integrates personality traits for enhanced SER performance.

Findings

Incorporating ground-truth personality traits improves valence recognition CCC from 0.698 to 0.785.

Automatically predicted personality traits enhance valence CCC to 0.776, an 11.17% improvement.

The approach confirms the effectiveness of personality-aware speech emotion recognition.

Abstract

This study investigates the interaction between personality traits and emotion expression, exploring how personality information can improve speech emotion recognition (SER). We collect the personality annotation for the IEMOCAP dataset, making it the first speech dataset that contains both emotion and personality annotations (PA-IEMOCAP), and enabling direct integration of personality traits into SER. Statistical analysis on this dataset identified significant correlations between personality traits and emotional expressions. To extract finegrained personality features, we propose a temporal interaction condition network (TICN), in which personality features are integrated with HuBERT-based acoustic features for SER. Experiments show that incorporating ground-truth personality traits significantly enhances valence recognition, improving the concordance correlation coefficient (CCC) from 0.698 to 0.785 compared to the baseline without personality information. For practical applications in dialogue systems where personality information about the user is unavailable, we develop a front-end module of automatic personality recognition. Using these automatically predicted traits as inputs to our proposed TICN model, we achieve a CCC of 0.776 for valence recognition, representing an 11.17% relative improvement over the baseline. These findings confirm the effectiveness of personality-aware SER and provide a solid foundation for further exploration in personality-aware speech processing applications.