A study on cross-corpus speech emotion recognition and data augmentation

TL;DR

This study examines how cross-corpus data augmentation affects speech emotion recognition models, showing that mixed training and augmentation improve stability and performance in diverse and unseen acoustic conditions.

Contribution

It provides empirical insights into the benefits of cross-corpus training and data augmentation for enhancing SER model robustness across different speakers and recording conditions.

Findings

Models trained on mixed corpora are more stable in mismatched conditions.

Data augmentation improves performance, especially in mismatched scenarios.

Performance drops 10-40% in mismatched conditions for single-corpus models.

Abstract

Models that can handle a wide range of speakers and acoustic conditions are essential in speech emotion recognition (SER). Often, these models tend to show mixed results when presented with speakers or acoustic conditions that were not visible during training. This paper investigates the impact of cross-corpus data complementation and data augmentation on the performance of SER models in matched (test-set from same corpus) and mismatched (test-set from different corpus) conditions. Investigations using six emotional speech corpora that include single and multiple speakers as well as variations in emotion style (acted, elicited, natural) and recording conditions are presented. Observations show that, as expected, models trained on single corpora perform best in matched conditions while performance decreases between 10-40% in mismatched conditions, depending on corpus specific features. Models trained on mixed corpora can be more stable in mismatched contexts, and the performance reductions range from 1 to 8% when compared with single corpus models in matched conditions. Data augmentation yields additional gains up to 4% and seem to benefit mismatched conditions more than matched ones.