Quality-Controlled Multimodal Emotion Recognition in Conversations with Identity-Based Transfer Learning and MAMBA Fusion

• URL: http://arxiv.org/abs/2511.14969v1
• Date: Tue, 18 Nov 2025 23:24:27 GMT
• Title: Quality-Controlled Multimodal Emotion Recognition in Conversations with Identity-Based Transfer Learning and MAMBA Fusion
• Abstract summary: We implement a quality control pipeline for datasets that validates speaker identity, audio-text alignment, and face detection.<n>We leverage transfer learning from speaker and face recognition, assuming that identity-discriminative embeddings capture not only stable acoustic and Facial traits but also person-specific patterns of emotional expression.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses data quality issues in multimodal emotion recognition in conversation (MERC) through systematic quality control and multi-stage transfer learning. We implement a quality control pipeline for MELD and IEMOCAP datasets that validates speaker identity, audio-text alignment, and face detection. We leverage transfer learning from speaker and face recognition, assuming that identity-discriminative embeddings capture not only stable acoustic and Facial traits but also person-specific patterns of emotional expression. We employ RecoMadeEasy(R) engines for extracting 512-dimensional speaker and face embeddings, fine-tune MPNet-v2 for emotion-aware text representations, and adapt these features through emotion-specific MLPs trained on unimodal datasets. MAMBA-based trimodal fusion achieves 64.8% accuracy on MELD and 74.3% on IEMOCAP. These results show that combining identity-based audio and visual embeddings with emotion-tuned text representations on a quality-controlled subset of data yields consistent competitive performance for multimodal emotion recognition in conversation and provides a basis for further improvement on challenging, low-frequency emotion classes.

Related papers

• Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation [63.94836524433559]
  DICE-Talk is a framework for disentangling identity with emotion and cooperating emotions with similar characteristics.<n>We develop a disentangled emotion embedder that jointly models audio-visual emotional cues through cross-modal attention.<n>Second, we introduce a correlation-enhanced emotion conditioning module with learnable Emotion Banks.<n>Third, we design an emotion discrimination objective that enforces affective consistency during the diffusion process.
  arXiv  Detail & Related papers  (2025-04-25T05:28:21Z)
• Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition [60.58049741496505]
  Speech Emotion Recognition (SER) plays a crucial role in enhancing human-computer interaction.<n>We propose a novel approach HuMP-CAT, which combines HuBERT, MFCC, and prosodic characteristics.<n>We show that, by fine-tuning the source model with a small portion of speech from the target datasets, HuMP-CAT achieves an average accuracy of 78.75%.
  arXiv  Detail & Related papers  (2025-01-06T14:31:25Z)
• Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning [55.127202990679976]
  We introduce the MERR dataset, containing 28,618 coarse-grained and 4,487 fine-grained annotated samples across diverse emotional categories. This dataset enables models to learn from varied scenarios and generalize to real-world applications. We propose Emotion-LLaMA, a model that seamlessly integrates audio, visual, and textual inputs through emotion-specific encoders.
  arXiv  Detail & Related papers  (2024-06-17T03:01:22Z)
• UMETTS: A Unified Framework for Emotional Text-to-Speech Synthesis with Multimodal Prompts [64.02363948840333]
  UMETTS is a novel framework that leverages emotional cues from multiple modalities to generate highly expressive and emotionally resonant speech.<n>EP-Align employs contrastive learning to align emotional features across text, audio, and visual modalities, ensuring a coherent fusion of multimodal information.<n>EMI-TTS integrates the aligned emotional embeddings with state-of-the-art TTS models to synthesize speech that accurately reflects the intended emotions.
  arXiv  Detail & Related papers  (2024-04-29T03:19:39Z)
• AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations [57.99479708224221]
  We propose a novel framework called AIMDiT to solve the problem of multimodal fusion of deep features. Experiments conducted using our AIMDiT framework on the public benchmark dataset MELD reveal 2.34% and 2.87% improvements in terms of the Acc-7 and w-F1 metrics.
  arXiv  Detail & Related papers  (2024-04-12T11:31:18Z)
• Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations [15.705757672984662]
  Multimodal Emotion Recognition in Conversations (MERC) is a significant development direction for machine intelligence. Many data in MERC naturally exhibit an imbalanced distribution of emotion categories, and researchers ignore the negative impact of imbalanced data on emotion recognition. We propose the Class Boundary Enhanced Representation Learning (CBERL) model to address the imbalanced distribution of emotion categories in raw data. We have conducted extensive experiments on the IEMOCAP and MELD benchmark datasets, and the results show that CBERL has achieved a certain performance improvement in the effectiveness of emotion recognition.
  arXiv  Detail & Related papers  (2023-12-11T12:35:17Z)
• HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition [41.837538440839815]
  We propose a hierarchical cross-attention model (HCAM) approach to multi-modal emotion recognition. The input to the model consists of two modalities, i) audio data, processed through a learnable wav2vec approach and, ii) text data represented using a bidirectional encoder representations from transformers (BERT) model. In order to incorporate contextual knowledge and the information across the two modalities, the audio and text embeddings are combined using a co-attention layer.
  arXiv  Detail & Related papers  (2023-04-14T03:25:00Z)
• Multi Modal Facial Expression Recognition with Transformer-Based Fusion Networks and Dynamic Sampling [1.983814021949464]
  We introduce a Modal Fusion Module (MFM) to fuse audio-visual information, where image and audio features are extracted from Swin Transformer. Our model has been evaluated in the Affective Behavior in-the-wild (ABAW) challenge of CVPR 2023.
  arXiv  Detail & Related papers  (2023-03-15T07:40:28Z)
• M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation [1.3864478040954673]
  We propose a Multi-modal Fusion Network (M2FNet) that extracts emotion-relevant features from visual, audio, and text modality. It employs a multi-head attention-based fusion mechanism to combine emotion-rich latent representations of the input data. The proposed feature extractor is trained with a novel adaptive margin-based triplet loss function to learn emotion-relevant features from the audio and visual data.
  arXiv  Detail & Related papers  (2022-06-05T14:18:58Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• An Attribute-Aligned Strategy for Learning Speech Representation [57.891727280493015]
  We propose an attribute-aligned learning strategy to derive speech representation that can flexibly address these issues by attribute-selection mechanism. Specifically, we propose a layered-representation variational autoencoder (LR-VAE), which factorizes speech representation into attribute-sensitive nodes. Our proposed method achieves competitive performances on identity-free SER and a better performance on emotionless SV.
  arXiv  Detail & Related papers  (2021-06-05T06:19:14Z)
• Self-Supervised learning with cross-modal transformers for emotion recognition [20.973999078271483]
  Self-supervised learning has shown improvements on tasks with limited labeled datasets in domains like speech and natural language. In this work, we extend self-supervised training to multi-modal applications.
  arXiv  Detail & Related papers  (2020-11-20T21:38:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.