Representation Learning with Graph Neural Networks for Speech Emotion Recognition

• URL: http://arxiv.org/abs/2208.09830v1
• Date: Sun, 21 Aug 2022 07:37:18 GMT
• Title: Representation Learning with Graph Neural Networks for Speech Emotion Recognition
• Authors: Junghun Kim, Jihie Kim
• Abstract summary: We present a Cosine similarity-based Graph Convolutional Network (CoGCN) that is robust to perturbation and noise. Experimental results show that our method outperforms state-of-the-art methods.
• Score: 0.7081604594416336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning expressive representation is crucial in deep learning. In speech emotion recognition (SER), vacuum regions or noises in the speech interfere with expressive representation learning. However, traditional RNN-based models are susceptible to such noise. Recently, Graph Neural Network (GNN) has demonstrated its effectiveness for representation learning, and we adopt this framework for SER. In particular, we propose a cosine similarity-based graph as an ideal graph structure for representation learning in SER. We present a Cosine similarity-based Graph Convolutional Network (CoGCN) that is robust to perturbation and noise. Experimental results show that our method outperforms state-of-the-art methods or provides competitive results with a significant model size reduction with only 1/30 parameters.

Related papers

• Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection [1.3673890873313355]
  Transformer-based models dominate NLP tasks like sentiment analysis, machine translation, and claim verification. In this work, we explore Graph Neural Networks (GNNs) and Hyperbolic Graph Neural Networks (HGNNs) as lightweight yet effective alternatives for Environmental Claim Detection.
  arXiv  Detail & Related papers  (2025-02-19T11:04:59Z)
• Graph Reasoning Networks [9.18586425686959]
  Graph Reasoning Networks (GRNs) is a novel approach to combine the strengths of fixed and learned graph representations and a reasoning module based on a differentiable satisfiability solver. Results on real-world datasets show comparable performance to GNNs. Experiments on synthetic datasets demonstrate the potential of the newly proposed method.
  arXiv  Detail & Related papers  (2024-07-08T10:53:49Z)
• Seeing in Words: Learning to Classify through Language Bottlenecks [59.97827889540685]
  Humans can explain their predictions using succinct and intuitive descriptions. We show that a vision model whose feature representations are text can effectively classify ImageNet images.
  arXiv  Detail & Related papers  (2023-06-29T00:24:42Z)
• Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective [53.999128831324576]
  Graph neural networks (GNNs) have pioneered advancements in graph representation learning. This study investigates the role of graph convolution within the context of feature learning theory.
  arXiv  Detail & Related papers  (2023-06-24T10:21:11Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
  This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths. In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
  arXiv  Detail & Related papers  (2020-12-09T12:40:13Z)
• GINet: Graph Interaction Network for Scene Parsing [58.394591509215005]
  We propose a Graph Interaction unit (GI unit) and a Semantic Context Loss (SC-loss) to promote context reasoning over image regions. The proposed GINet outperforms the state-of-the-art approaches on the popular benchmarks, including Pascal-Context and COCO Stuff.
  arXiv  Detail & Related papers  (2020-09-14T02:52:45Z)
• Compact Graph Architecture for Speech Emotion Recognition [0.0]
  A compact, efficient and scalable way to represent data is in the form of graphs. We construct a Graph Convolution Network (GCN)-based architecture that can perform an accurate graph convolution. Our model achieves comparable performance to the state-of-the-art with significantly fewer learnable parameters.
  arXiv  Detail & Related papers  (2020-08-05T12:09:09Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• Gradient-Adjusted Neuron Activation Profiles for Comprehensive Introspection of Convolutional Speech Recognition Models [1.6752182911522515]
  We introduce Gradient-adjusted Neuron Activation Profiles (GradNAPs) as means to interpret features and representations in Deep Neural Networks. GradNAPs are characteristic responses of ANNs to particular groups of inputs, which incorporate the relevance of neurons for prediction. We show how to utilize GradNAPs to gain insight about how data is processed in ANNs.
  arXiv  Detail & Related papers  (2020-02-19T11:59:36Z)
• Comparison of Syntactic and Semantic Representations of Programs in Neural Embeddings [1.0878040851638]
  It compares graph convolutional networks using different graph representations in the task of program embedding. It shows that the sparsity of control flow graphs and the implicit aggregation of graph convolutional networks cause these models to perform worse than naive models.
  arXiv  Detail & Related papers  (2020-01-24T21:30:03Z)

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.