Fine-grained Disentangled Representation Learning for Multimodal Emotion Recognition

Multimodal emotion recognition (MMER) is an active research field that aims to accurately recognize human emotions by fusing multiple perceptual modalities. However, inherent heterogeneity across modalities introduces distribution gaps and information redundancy, posing significant challenges for MMER. In this paper, we propose a novel fine-grained disentangled representation learning (FDRL) framework to address these challenges. Specifically, we design modality-shared and modality-private encoders to project each modality into modality-shared and modality-private subspaces, respectively. In the shared subspace, we introduce a fine-grained alignment component to learn modality-shared representations, thus capturing modal consistency. Subsequently, we tailor a fine-grained disparity component to constrain the private subspaces, thereby learning modality-private representations and enhancing their diversity. Lastly, we introduce a fine-grained predictor component to ensure that the labels of the output representations from the encoders remain unchanged. Experimental results on the IEMOCAP dataset show that FDRL outperforms the state-of-the-art methods, achieving 78.34% and 79.44% on WAR and UAR, respectively.

kNN-CTC: Enhancing ASR via Retrieval of CTC Pseudo Labels

The success of retrieval-augmented language models in various natural language processing (NLP) tasks has been constrained in automatic speech recognition (ASR) applications due to challenges in constructing fine-grained audio-text datastores. This paper presents kNN-CTC, a novel approach that overcomes these challenges by leveraging Connectionist Temporal Classification (CTC) pseudo labels to establish frame-level audio-text key-value pairs, circumventing the need for precise ground truth alignments. We further introduce a skip-blank strategy, which strategically ignores CTC blank frames, to reduce datastore size. kNN-CTC incorporates a k-nearest neighbors retrieval mechanism into pre-trained CTC ASR systems, achieving significant improvements in performance. By incorporating a k-nearest neighbors retrieval mechanism into pre-trained CTC ASR systems and leveraging a fine-grained, pruned datastore, kNN-CTC consistently achieves substantial improvements in performance under various experimental settings. Our code is available at https://github.com/NKU-HLT/KNN-CTC.