Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm

• URL: http://arxiv.org/abs/2506.02610v1
• Date: Tue, 03 Jun 2025 08:29:10 GMT
• Title: Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm
• Authors: Zhaoyang Li, Jie Wang, XiaoXiao Li, Wangjie Li, Longjie Luo, Lin Li, Qingyang Hong,
• Abstract summary: The proposed framework comprises two key components: (1) a graph attention network that refines speaker embeddings and node connections by aggregating information from neighboring nodes, and (2) a label propagation algorithm that assigns multiple community labels to each node, enabling simultaneous clustering and overlapping community detection.<n> Experimental results show that the proposed method significantly reduces the Diarization Error Rate (DER), achieving a state-of-the-art 15.94% DER on the DIHARD-III dataset without oracle Voice Activity Detection (VAD) and an impressive 11.07% with oracle VAD.
• Score: 28.940391846079116
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In speaker diarization, traditional clustering-based methods remain widely used in real-world applications. However, these methods struggle with the complex distribution of speaker embeddings and overlapping speech segments. To address these limitations, we propose an Overlapping Community Detection method based on Graph Attention networks and the Label Propagation Algorithm (OCDGALP). The proposed framework comprises two key components: (1) a graph attention network that refines speaker embeddings and node connections by aggregating information from neighboring nodes, and (2) a label propagation algorithm that assigns multiple community labels to each node, enabling simultaneous clustering and overlapping community detection. Experimental results show that the proposed method significantly reduces the Diarization Error Rate (DER), achieving a state-of-the-art 15.94% DER on the DIHARD-III dataset without oracle Voice Activity Detection (VAD), and an impressive 11.07% with oracle VAD.

Related papers

• Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation [13.802788788420175]
  Correspondence matching plays a crucial role in numerous robotics applications. This paper addresses the limitations of deep feature matching (DFM), a state-of-the-art (SoTA) plug-and-play correspondence matching approach. Our proposed method achieves an overall performance in terms of mean matching accuracy of 0.68, 0.92, and 0.95 with respect to the tolerances of 1, 3, and 5 pixels, respectively.
  arXiv  Detail & Related papers  (2024-03-08T15:32:18Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement [53.044703127757295]
  Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims at learning modality-invariant features from unlabeled cross-modality dataset. We propose a Dual Optimal Transport Label Assignment (DOTLA) framework to simultaneously assign the generated labels from one modality to its counterpart modality. The proposed DOTLA mechanism formulates a mutual reinforcement and efficient solution to cross-modality data association, which could effectively reduce the side-effects of some insufficient and noisy label associations.
  arXiv  Detail & Related papers  (2023-05-22T04:40:30Z)
• Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification [17.284276598514502]
  We propose a novel progressive subgraph clustering algorithm based on multi-model voting and double-Gaussian based assessment. To prevent disastrous clustering results, we adopt an iterative approach that progressively increases k and employs a double-Gaussian based assessment algorithm.
  arXiv  Detail & Related papers  (2023-05-22T04:26:18Z)
• Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID [56.573905143954015]
  We propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2023-05-22T03:27:46Z)
• Learn to Cluster Faces with Better Subgraphs [13.511058277653122]
  Face clustering can provide pseudo-labels to the massive unlabeled face data. Existing clustering methods aggregate features within subgraphs based on a uniform threshold or a learned cutoff position. This work proposed an efficient neighborhood-aware subgraph adjustment method that can significantly reduce the noise.
  arXiv  Detail & Related papers  (2023-04-21T09:18:55Z)
• Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization [41.30830281043803]
  We propose a novel Supervised HierArchical gRaph Clustering algorithm (SHARC) for speaker diarization. In this paper, we introduce a hierarchical structure using Graph Neural Network (GNN) to perform supervised clustering. The supervised clustering is performed using node densities and edge existence probabilities to merge the segments until convergence.
  arXiv  Detail & Related papers  (2023-02-24T16:16:41Z)
• Speaker Embedding-aware Neural Diarization: a Novel Framework for Overlapped Speech Diarization in the Meeting Scenario [51.5031673695118]
  We reformulate overlapped speech diarization as a single-label prediction problem. We propose the speaker embedding-aware neural diarization (SEND) system.
  arXiv  Detail & Related papers  (2022-03-18T06:40:39Z)
• Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions [123.07967420310796]
  This paper bridges the gap by proposing a pairwise framework for noisy node classification on graphs. PI-GNN relies on the PI as a primary learning proxy in addition to the pointwise learning from the noisy node class labels. Our proposed framework PI-GNN contributes two novel components: (1) a confidence-aware PI estimation model that adaptively estimates the PI labels, and (2) a decoupled training approach that leverages the estimated PI labels.
  arXiv  Detail & Related papers  (2021-06-14T14:23:08Z)
• Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments [75.8001929811943]
  We introduce a realistic and challenging, multi-source and multi-room acoustic environment. We present an improved clustering control strategy that takes into account the variability of the acoustic scene. The proposed approach is optimized using clustering-based measures and validated via a network-wide classification task.
  arXiv  Detail & Related papers  (2021-06-07T14:51:39Z)

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.