Paper Citation Record · LEDGER

End-to-End Diarization utilizing Attractor Deep Clustering

As of 19 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2506.11090.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.11090 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:29:43.827276Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:29:43.217153Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T10:29:44.136911Z

Reference resolution

46 of 46 outbound references displayed

verified exact1
verified fuzzy20
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 11500e78-5d02-4b95-9795-4387ea133a80 · outbound

This paper cites End-to-End Diarization utilizing Attractor Deep Clustering.

End-to-End Diarization utilizing Attractor Deep Clustering End-to-End Diarization utilizing Attractor Deep Clustering

Reference 1

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Observation c548d584-e14d-4e93-92f4-6cf04635097f · outbound

This paper cites Early methods often combined Gaussian Mixture Models (GMM) or i-vector.

End-to-End Diarization utilizing Attractor Deep Clustering Early methods often combined Gaussian Mixture Models (GMM) or i-vector

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.736832Z

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Observation d110a76b-c8ea-41f5-85b1-1a59c8c06fc1 · outbound

This paper cites grayscale.

End-to-End Diarization utilizing Attractor Deep Clustering grayscale

Reference 3

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 4

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Observation a9fd2ee9-6575-4ddc-a2ce-530b6dc57364 · outbound

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Reference 5

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 6

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Observation 8834b4c5-1a40-437f-800c-df9a3b193700 · outbound

This paper cites This captures global temporal relationships.

End-to-End Diarization utilizing Attractor Deep Clustering This captures global temporal relationships

Reference 7

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Observation 7bb36ca2-d07b-4f9c-9dc1-89633b386358 · outbound

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 8

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Observation cd71b720-f828-4184-813b-d3d0f0f8db81 · outbound

This paper cites Latte: Latent attention for linear time transformers,.

End-to-End Diarization utilizing Attractor Deep Clustering Latte: Latent attention for linear time transformers,

Reference 9

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Reference 10

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Observation 8fac7fe1-52b4-4d1f-a32b-6065c5ea080c · outbound

This paper cites Each conformer block outputs Batch × Time × 256, which is then used to compute speaker logits or passed to subsequent layers (Section 3.4).

End-to-End Diarization utilizing Attractor Deep Clustering Each conformer block outputs Batch × Time × 256, which is then used to compute speaker logits or passed to subsequent layers (Section 3.4)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.621125Z

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 12

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Observation 521ba150-d203-4ab4-974f-8204d181a30d · outbound

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 13

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Observation ec76142c-2a38-4fab-b85d-0743f3af03c9 · outbound

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 14

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Observation 76c90646-124d-4972-aeef-38079dfc7675 · outbound

This paper cites This ensures each block sees a global summary of all prior representations, acting as a global residual connection, and smooths training convergence.

End-to-End Diarization utilizing Attractor Deep Clustering This ensures each block sees a global summary of all prior representations, acting as a global residual connection, and smooths training convergence

Reference 15

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Observation 93f77f3c-0e0e-45aa-bfa3-d46a5afd0668 · outbound

This paper cites Data Preparation Following the example of prior EEND studies [6], we prepare data from the CALLHOME and Switchboard-2 (Phase II, III) corpora.

End-to-End Diarization utilizing Attractor Deep Clustering Data Preparation Following the example of prior EEND studies [6], we prepare data from the CALLHOME and Switchboard-2 (Phase II, III) corpora

Reference 16

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 18

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Observation 5b35f5bc-052c-4932-9c30-503c27eb8c6b · outbound

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 19

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 21

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Reference 22

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End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 23

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This paper cites an unresolved cited work.

End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:29:44.447116Z

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Observation b6db43a7-2db0-453f-a29b-0c953ff07418 · outbound

This paper cites an unresolved cited work.

End-to-End Diarization utilizing Attractor Deep Clustering Unresolved cited work

Reference 25

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Observation ae7af194-ffaf-44b2-9240-2b7f8130efe9 · outbound

This paper cites Speaker diarization: A review of recent research,.

End-to-End Diarization utilizing Attractor Deep Clustering Speaker diarization: A review of recent research,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.403067Z

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Observation 3078046c-a3c3-4a02-b0d4-826b352b897d · outbound

This paper cites A review of speaker diarization: Recent advances with deep learning,.

End-to-End Diarization utilizing Attractor Deep Clustering A review of speaker diarization: Recent advances with deep learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.379873Z

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Observation 77e66e23-493b-443b-9267-8d333d1244a8 · outbound

This paper cites End-to-end neural speaker diarization with permutation-free ob- jectives,.

End-to-End Diarization utilizing Attractor Deep Clustering End-to-end neural speaker diarization with permutation-free ob- jectives,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.361000Z

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Observation a3c55f81-5e01-40e8-928c-7e27a0a40384 · outbound

This paper cites Deep clustering: Discriminative embeddings for segmentation and sep- aration,.

End-to-End Diarization utilizing Attractor Deep Clustering Deep clustering: Discriminative embeddings for segmentation and sep- aration,

Reference 29

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Observation 413206bf-3bcb-413a-bb56-736b5831aac7 · outbound

This paper cites End-to-end speaker diarization for an unknown number of speak- ers with encoder-decoder based attractors,.

End-to-End Diarization utilizing Attractor Deep Clustering End-to-end speaker diarization for an unknown number of speak- ers with encoder-decoder based attractors,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.328812Z

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Observation 4765c00d-ab15-4819-975d-be0ca922e0c8 · outbound

This paper cites Neu- ral diarization with non-autoregressive intermediate attractors,.

End-to-End Diarization utilizing Attractor Deep Clustering Neu- ral diarization with non-autoregressive intermediate attractors,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.315673Z

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Observation b82de37b-7209-4d47-a86a-a7524e299d69 · outbound

This paper cites Improving neural diarization through speaker attribute attractors and local dependency modeling,.

End-to-End Diarization utilizing Attractor Deep Clustering Improving neural diarization through speaker attribute attractors and local dependency modeling,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.301533Z

Source-reported events for the cited work

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Observation 783b8bfd-ccb1-4b57-b1ac-a07611b30447 · outbound

This paper cites Permutation invariant training of deep models for speaker-independent multi-talker speech separation,.

End-to-End Diarization utilizing Attractor Deep Clustering Permutation invariant training of deep models for speaker-independent multi-talker speech separation,

Reference 33

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Observation 0e93d1ca-ec03-4dcb-b2fc-b7f8b8c3f930 · outbound

This paper cites Front-end factor analysis for speaker verification,.

End-to-End Diarization utilizing Attractor Deep Clustering Front-end factor analysis for speaker verification,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.287571Z

Source-reported events for the cited work

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Observation d50638e7-adc0-48fc-b2f5-3021960835f9 · outbound

This paper cites Speaker diarization with PLDA i-vector scoring and unsupervised calibration,.

End-to-End Diarization utilizing Attractor Deep Clustering Speaker diarization with PLDA i-vector scoring and unsupervised calibration,

Reference 35

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Observation c745cea2-85e8-41b8-af08-4b1dbaff9111 · outbound

This paper cites X-vectors: Robust DNN embeddings for speaker recog- nition,.

End-to-End Diarization utilizing Attractor Deep Clustering X-vectors: Robust DNN embeddings for speaker recog- nition,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:43.732296Z

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Observation ff89c678-88c6-47f3-9b21-1fdad8c5bcb1 · outbound

This paper cites Conformer: Convolution-augmented transformer for speech recognition,.

End-to-End Diarization utilizing Attractor Deep Clustering Conformer: Convolution-augmented transformer for speech recognition,

Reference 37

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Observation 8e90dc0e-e75d-4cf8-b0a6-3de7a8823477 · outbound

This paper cites Robust end-to-end speaker di- arization with conformer and additive margin penalty,.

End-to-End Diarization utilizing Attractor Deep Clustering Robust end-to-end speaker di- arization with conformer and additive margin penalty,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.247463Z

Source-reported events for the cited work

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Observation 67d60f47-24a1-4c4f-bbd4-15617a21b880 · outbound

This paper cites End-to-end neural diarization: From transformer to conformer,.

End-to-End Diarization utilizing Attractor Deep Clustering End-to-end neural diarization: From transformer to conformer,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.233283Z

Source-reported events for the cited work

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Observation e3de86cf-80ef-4d2d-b7e1-5a913d3ea0a8 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

End-to-End Diarization utilizing Attractor Deep Clustering Linformer: Self-Attention with Linear Complexity

Reference 40

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Observation 8937c6c3-8d19-4221-ac21-38edf56ecbd1 · outbound

This paper cites Deepseek-V3 technical report,.

End-to-End Diarization utilizing Attractor Deep Clustering Deepseek-V3 technical report,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.215604Z

Source-reported events for the cited work

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Observation 4550756e-97a6-4c54-87dd-1d14823ad0c0 · outbound

This paper cites Single-channel multi-speaker separation using deep clustering,.

End-to-End Diarization utilizing Attractor Deep Clustering Single-channel multi-speaker separation using deep clustering,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.199786Z

Source-reported events for the cited work

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Observation 540b6679-623d-49d4-b972-2d5777ae37b2 · outbound

This paper cites Alternative objective functions for deep clustering,.

End-to-End Diarization utilizing Attractor Deep Clustering Alternative objective functions for deep clustering,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:43.805407Z

Source-reported events for the cited work

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Observation 69aa6e05-7c72-490c-852b-f8835bf21ff7 · outbound

This paper cites MUSAN: A Music, Speech, and Noise Corpus.

End-to-End Diarization utilizing Attractor Deep Clustering MUSAN: A Music, Speech, and Noise Corpus

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:43.821301Z

Source-reported events for the cited work

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Observation 3481969e-7b8b-42b5-9306-ad1c4cf2adee · outbound

This paper cites From simu- lated mixtures to simulated conversations as training data for end- to-end neural diarization,.

End-to-End Diarization utilizing Attractor Deep Clustering From simu- lated mixtures to simulated conversations as training data for end- to-end neural diarization,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.168490Z

Source-reported events for the cited work

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Observation 9ea42fba-3a1a-431a-a768-5a475b639604 · outbound

This paper cites DeepSeek-V3 Technical Report.

End-to-End Diarization utilizing Attractor Deep Clustering DeepSeek-V3 Technical Report

Reference 2024

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Source-reported events for the cited work

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Pith citing papers

Observation 11500e78-5d02-4b95-9795-4387ea133a80 · inbound