HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition

• URL: http://arxiv.org/abs/2304.06910v2
• Date: Tue, 9 Jan 2024 11:45:34 GMT
• Title: HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition
• Authors: Soumya Dutta and Sriram Ganapathy
• Abstract summary: 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.
• Score: 41.837538440839815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion recognition in conversations is challenging due to the multi-modal nature of the emotion expression. We propose a hierarchical cross-attention model (HCAM) approach to multi-modal emotion recognition using a combination of recurrent and co-attention neural network models. 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. The audio and text representations are processed using a set of bi-directional recurrent neural network layers with self-attention that converts each utterance in a given conversation to a fixed dimensional embedding. 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 that attempts to weigh the utterance level embeddings relevant to the task of emotion recognition. The neural network parameters in the audio layers, text layers as well as the multi-modal co-attention layers, are hierarchically trained for the emotion classification task. We perform experiments on three established datasets namely, IEMOCAP, MELD and CMU-MOSI, where we illustrate that the proposed model improves significantly over other benchmarks and helps achieve state-of-art results on all these datasets.

Related papers

• 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)
• AMuSE: Adaptive Multimodal Analysis for Speaker Emotion Recognition in Group Conversations [39.79734528362605]
  Multimodal Attention Network captures cross-modal interactions at various levels of spatial abstraction. AMuSE model condenses both spatial and temporal features into two dense descriptors: speaker-level and utterance-level.
  arXiv  Detail & Related papers  (2024-01-26T19:17:05Z)
• Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition [14.639340916340801]
  We propose a novel Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive for Multimodal Emotion Recognition (AR-IIGCN) method. Firstly, we input video, audio, and text features into a multi-layer perceptron (MLP) to map them into separate feature spaces. Secondly, we build a generator and a discriminator for the three modal features through adversarial representation. Thirdly, we introduce contrastive graph representation learning to capture intra-modal and inter-modal complementary semantic information.
  arXiv  Detail & Related papers  (2023-12-28T01:57:26Z)
• Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation [9.817888267356716]
  multimodal Emotion Recognition in Conversation (ERC) faces two problems. Deep emotion cues extraction was performed on modalities with strong representation ability. Feature filters were designed as multimodal prompt information for modalities with weak representation ability. MPT embeds multimodal fusion information into each attention layer of the Transformer.
  arXiv  Detail & Related papers  (2023-10-04T13:54:46Z)
• Learning to Model Multimodal Semantic Alignment for Story Visualization [58.16484259508973]
  Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story. Current works face the problem of semantic misalignment because of their fixed architecture and diversity of input modalities. We explore the semantic alignment between text and image representations by learning to match their semantic levels in the GAN-based generative model.
  arXiv  Detail & Related papers  (2022-11-14T11:41:44Z)
• 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)
• Multimodal Emotion Recognition with High-level Speech and Text Features [8.141157362639182]
  We propose a novel cross-representation speech model to perform emotion recognition on wav2vec 2.0 speech features. We also train a CNN-based model to recognize emotions from text features extracted with Transformer-based models. Our method is evaluated on the IEMOCAP dataset in a 4-class classification problem.
  arXiv  Detail & Related papers  (2021-09-29T07:08:40Z)
• Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-organized Operational Layer [92.37382674655942]
  We propose a network layer to enhance the speech command recognition capability of a lightweight network. The employed method borrows the ideas of Taylor expansion and quadratic forms to construct a better representation of features in both input and hidden layers. This richer representation results in recognition accuracy improvement as shown by extensive experiments on Google speech commands (GSC) and synthetic speech commands (SSC) datasets.
  arXiv  Detail & Related papers  (2020-11-23T14:40:18Z)
• Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers [89.00926092864368]
  We present a semantics-controlled multi-modal shuffled Transformer reasoning framework for the audio-visual scene aware dialog task. We also present a novel dynamic scene graph representation learning pipeline that consists of an intra-frame reasoning layer producing-semantic graph representations for every frame. Our results demonstrate state-of-the-art performances on all evaluation metrics.
  arXiv  Detail & Related papers  (2020-07-08T02:00:22Z)

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.