M4SER: Multimodal, Multirepresentation, Multitask, and Multistrategy Learning for Speech Emotion Recognition

Multimodal speech emotion recognition (SER) has emerged as pivotal for improving human-machine interaction. Researchers are increasingly leveraging both speech and textual information obtained through automatic speech recognition (ASR) to comprehensively recognize emotional states from speakers. Although this approach reduces reliance on human-annotated text data, ASR errors possibly degrade emotion recognition performance. To address this challenge, in our previous work, we introduced two auxiliary tasks, namely, ASR error detection and ASR error correction, and we proposed a novel multimodal fusion (MF) method for learning modality-specific and modality-invariant representations across different modalities. Building on this foundation, in this paper, we introduce two additional training strategies. First, we propose an adversarial network to enhance the diversity of modality-specific representations. Second, we introduce a label-based contrastive learning strategy to better capture emotional features. We refer to our proposed method as M4SER and validate its superiority over state-of-the-art methods through extensive experiments using IEMOCAP and MELD datasets.