Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques

• URL: http://arxiv.org/abs/2308.04517v1
• Date: Fri, 4 Aug 2023 06:20:42 GMT
• Title: Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques
• Authors: Samiul Islam, Md. Maksudul Haque, Abu Jobayer Md. Sadat
• Abstract summary: This research proposes an ensemble model that combines Graph Convolutional Networks (GCN) for processing textual data and the HuBERT transformer for analyzing audio signals. By combining GCN and HuBERT, our ensemble model can leverage the strengths of both approaches. Results indicate that the combined model can overcome the limitations of traditional methods, leading to enhanced accuracy in recognizing emotions from speech.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional approaches in speech emotion recognition, such as LSTM, CNN, RNN, SVM, and MLP, have limitations such as difficulty capturing long-term dependencies in sequential data, capturing the temporal dynamics, and struggling to capture complex patterns and relationships in multimodal data. This research addresses these shortcomings by proposing an ensemble model that combines Graph Convolutional Networks (GCN) for processing textual data and the HuBERT transformer for analyzing audio signals. We found that GCNs excel at capturing Long-term contextual dependencies and relationships within textual data by leveraging graph-based representations of text and thus detecting the contextual meaning and semantic relationships between words. On the other hand, HuBERT utilizes self-attention mechanisms to capture long-range dependencies, enabling the modeling of temporal dynamics present in speech and capturing subtle nuances and variations that contribute to emotion recognition. By combining GCN and HuBERT, our ensemble model can leverage the strengths of both approaches. This allows for the simultaneous analysis of multimodal data, and the fusion of these modalities enables the extraction of complementary information, enhancing the discriminative power of the emotion recognition system. The results indicate that the combined model can overcome the limitations of traditional methods, leading to enhanced accuracy in recognizing emotions from speech.

Related papers

• Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations [8.107561045241445]
  We propose an Efficient Long-distance Latent Relation-aware Graph Neural Network (ELR-GNN) for multi-modal emotion recognition in conversations. ELR-GNN achieves state-of-the-art performance on the benchmark IEMOCAP and MELD, with running times reduced by 52% and 35%, respectively.
  arXiv  Detail & Related papers  (2024-06-27T15:54:12Z)
• Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum [13.81570624162769]
  We propose a Graph-Spectrum-based Multimodal Consistency and Complementary collaborative learning framework GS-MCC. First, GS-MCC uses a sliding window to construct a multimodal interaction graph to model conversational relationships. Then, GS-MCC uses contrastive learning to construct self-supervised signals that reflect complementarity and consistent semantic collaboration.
  arXiv  Detail & Related papers  (2024-04-27T10:47:07Z)
• Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction [2.1261712640167856]
  Emotion recognition is a crucial task for human conversation understanding. We propose an input Temporal Graph Neural Network with Cross-Modality Interaction (CORECT) CORECT effectively captures conversation-level cross-modality interactions and utterance-level temporal dependencies.
  arXiv  Detail & Related papers  (2023-11-08T07:46:25Z)
• A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning [0.800062359410795]
  We present a novel paradigm for contextualized Emotion Recognition using Graph Convolutional Network with Reinforcement Learning (conER-GRL) Conversations are partitioned into smaller groups of utterances for effective extraction of contextual information. The system uses Gated Recurrent Units (GRU) to extract multimodal features from these groups of utterances.
  arXiv  Detail & Related papers  (2023-10-24T14:31:17Z)
• EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge [0.0]
  We present Explainable Multimodal Emotion Recognition with Situational Knowledge (EMERSK) EMERSK is a general system for human emotion recognition and explanation using visual information. Our system can handle multiple modalities, including facial expressions, posture, and gait in a flexible and modular manner.
  arXiv  Detail & Related papers  (2023-06-14T17:52:37Z)
• End-to-End Active Speaker Detection [58.7097258722291]
  We propose an end-to-end training network where feature learning and contextual predictions are jointly learned. We also introduce intertemporal graph neural network (iGNN) blocks, which split the message passing according to the main sources of context in the ASD problem. Experiments show that the aggregated features from the iGNN blocks are more suitable for ASD, resulting in state-of-the art performance.
  arXiv  Detail & Related papers  (2022-03-27T08:55:28Z)
• 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)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks [82.54695985117783]
  We investigate the suitability of state-of-the-art deep learning architectures for continuous emotion recognition using long video sequences captured in-the-wild. We have developed and evaluated convolutional recurrent neural networks combining 2D-CNNs and long short term-memory units, and inflated 3D-CNN models, which are built by inflating the weights of a pre-trained 2D-CNN model during fine-tuning.
  arXiv  Detail & Related papers  (2020-11-18T13:42:05Z)
• Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence [48.765579605145454]
  We propose to explicitly segment target text into fragment units and align them with their data correspondences. The resulting architecture maintains the same expressive power as neural attention models. On both E2E and WebNLG benchmarks, we show the proposed model consistently outperforms its neural attention counterparts.
  arXiv  Detail & Related papers  (2020-05-03T14:28:28Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)

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.