Chung-Soo Ahn

Chung-Soo Ahn, Jagath C. Rajapakse, Rajib Rana

While speech emotion recognition (SER) research has made significant progress, achieving generalization across various corpora continues to pose a problem. We propose a novel domain adaptation technique that embodies a multitask framework with SER as the primary task, and contrastive learning and information maximisation loss as auxiliary tasks, underpinned by fine-tuning of transformers pre-trained on large language models. Empirical results obtained through experiments on well-established datasets like IEMOCAP and MSP-IMPROV, illustrate that our proposed model achieves state-of-the-art performance in SER within cross-corpus scenarios.

Chung-Soo Ahn, Rajib Rana, Sunil Sivadas et al.

Although speech emotion recognition (SER) research has been advanced, thanks to deep learning methods, it still suffers from obtaining inputs from large quality-labelled training data. Data augmentation methods have been attempted to mitigate this issue, generative models have shown success among them recently. We propose a data augmentation framework that is aided by cross-modal information transfer and mutual information regularization. Mutual information based metric can serve as an indicator for the quality. Furthermore, we expand this data augmentation scope to multimodal inputs, thanks to mutual information ensureing dependency between modalities. Our framework was tested on three benchmark datasets: IEMOCAP, MSP-IMPROV and MSP-Podcast. The implementation was designed to generate input features that are fed into last layer for emotion classification. Our framework improved the performance of emotion prediction against existing works. Also, we discovered that our framework is able to generate new inputs without any cross-modal information.