Paper Citation Record · LEDGER

Clustering-based hard negative sampling for supervised contrastive speaker verification

As of 23 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2507.17540.

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

pith.paper-citation-record.v1
2507.17540 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:51:38.782649Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:51:35.536746Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T14:51:38.904076Z

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy26
unresolved6
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 876ffd37-7037-46df-9647-83dcdd509b33 · outbound

This paper cites Clustering-based hard negative sampling for supervised contrastive speaker verification.

Clustering-based hard negative sampling for supervised contrastive speaker verification Clustering-based hard negative sampling for supervised contrastive speaker verification

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T14:51:38.952983Z

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Observation b587d3cb-c5be-479f-9cdd-09c6771fde15 · outbound

This paper cites We use a contrastive loss function during the training pro- cess, which calculates the relationship between speaker repre- sentations on a within-batch basis.

Clustering-based hard negative sampling for supervised contrastive speaker verification We use a contrastive loss function during the training pro- cess, which calculates the relationship between speaker repre- sentations on a within-batch basis

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:42.560019Z

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Observation f2fdfb4f-1ef8-4a09-8542-ce13caba506e · outbound

This paper cites For a fair comparison, in all experiments, we use the exact same model, data and training parameters.

Clustering-based hard negative sampling for supervised contrastive speaker verification For a fair comparison, in all experiments, we use the exact same model, data and training parameters

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:42.550990Z

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Observation 8bbad764-55e6-4988-91f6-d99bf5e49628 · outbound

This paper cites The two variables in the algorithm are the number of clusters (which also correlates with the average cluster size) and hard ratio.

Clustering-based hard negative sampling for supervised contrastive speaker verification The two variables in the algorithm are the number of clusters (which also correlates with the average cluster size) and hard ratio

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:42.540960Z

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Observation 90a63a85-f847-4bb0-9301-e6c568e17067 · outbound

This paper cites an unresolved cited work.

Clustering-based hard negative sampling for supervised contrastive speaker verification Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:51:42.532138Z

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Observation 2ad0555e-58c1-4214-b812-fa1ad3bb0d17 · outbound

This paper cites Deep Neural Network Embeddings for Text-Independent Speaker Verification,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Deep Neural Network Embeddings for Text-Independent Speaker Verification,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:42.523166Z

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Observation ccd59a6d-a16c-43af-bafb-7f4a6b64596d · outbound

This paper cites Densely Connected Time Delay Neu- ral Network for Speaker Verification,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Densely Connected Time Delay Neu- ral Network for Speaker Verification,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:42.514497Z

Source-reported events for the cited work

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Observation 28eb10e9-3b17-41b2-b5c2-da0ae82aa360 · outbound

This paper cites X-vectors: Robust dnn embeddings for speaker recognition,.

Clustering-based hard negative sampling for supervised contrastive speaker verification X-vectors: Robust dnn embeddings for speaker recognition,

Reference 8

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Source-reported events for the cited work

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Observation 6dd3ead1-7224-44a3-9407-a53c003f7582 · outbound

This paper cites ECAPA-TDNN: Emphasized channel attention, propaga- tion and aggregation in TDNN based speaker verifica- tion,.

Clustering-based hard negative sampling for supervised contrastive speaker verification ECAPA-TDNN: Emphasized channel attention, propaga- tion and aggregation in TDNN based speaker verifica- tion,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:36.371353Z

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Observation 00ea6303-432a-41ca-bc60-de30a7c9f000 · outbound

This paper cites Wavlm: Large-scale self-supervised pre-training for full stack speech processing,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Wavlm: Large-scale self-supervised pre-training for full stack speech processing,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:42.500042Z

Source-reported events for the cited work

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Observation ce3abbed-dfbd-4a7a-8457-2bddc4e7c5c1 · outbound

This paper cites Arcface: Additive angular margin loss for deep face recognition,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Arcface: Additive angular margin loss for deep face recognition,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:42.491933Z

Source-reported events for the cited work

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Observation 94a24d4a-f850-46f6-b5c9-8b839433da75 · outbound

This paper cites Discriminative speaker representation via contrastive learning with class-aware atten- tion in angular space,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Discriminative speaker representation via contrastive learning with class-aware atten- tion in angular space,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:42.483518Z

Source-reported events for the cited work

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Observation 099e6d39-d674-4eb9-8ad7-0360a9545f06 · outbound

This paper cites Contrastive learning for improving end-to-end speaker verification,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Contrastive learning for improving end-to-end speaker verification,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:42.474101Z

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Observation 1a61dc54-a995-4c98-8d85-aae3e2e0ed84 · outbound

This paper cites In Defence of Metric Learning for Speaker Recognition,.

Clustering-based hard negative sampling for supervised contrastive speaker verification In Defence of Metric Learning for Speaker Recognition,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:42.326601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 72c0bdb3-7517-4400-bdd5-45e5db351675 · outbound

This paper cites Contrastive self-supervised learning for text-independent speaker verification,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Contrastive self-supervised learning for text-independent speaker verification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:42.176161Z

Source-reported events for the cited work

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Observation 5c7acf92-473e-4381-be22-8ed2b19f78e1 · outbound

This paper cites Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Con- trastive Learning,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Con- trastive Learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:41.844804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1b79b24f-f368-4400-9b9d-cf7dd883e204 · outbound

This paper cites The IDLAB VoxCeleb Speaker Recognition Challenge 2020 System Description.

Clustering-based hard negative sampling for supervised contrastive speaker verification The IDLAB VoxCeleb Speaker Recognition Challenge 2020 System Description

Reference 17

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Observation bfbe2293-7cbc-4199-8522-f382f0dc2c9d · outbound

This paper cites Self-supervised speaker recognition with loss-gated learning,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Self-supervised speaker recognition with loss-gated learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:41.528291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5217116f-09d9-4fd0-9b8b-bc6a0bcc8033 · outbound

This paper cites Experimenting with Additive Margins for Contrastive Self-Supervised Speaker Verification,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Experimenting with Additive Margins for Contrastive Self-Supervised Speaker Verification,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:41.235504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 232bef23-26ae-44fe-8544-ad096c711df1 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Clustering-based hard negative sampling for supervised contrastive speaker verification A simple framework for contrastive learning of visual representations,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:41.037182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 066cd079-8e6a-442f-841c-99cffccc214d · outbound

This paper cites Supervised contrastive learning,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Supervised contrastive learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:40.865513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b97b25ff-e3a8-41db-bcdb-8febd8d43ac3 · outbound

This paper cites Contrastive speaker representation learning with hard negative sampling for speaker recognition,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Contrastive speaker representation learning with hard negative sampling for speaker recognition,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:40.600120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 980ce759-c3d0-4e6c-9938-ce05799c57bc · outbound

This paper cites Contrastive learning with hard negative samples,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Contrastive learning with hard negative samples,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:40.412214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 82e8e723-99c2-46a8-86cf-63e7515481b6 · outbound

This paper cites Supervised con- trastive learning with hard negative samples,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Supervised con- trastive learning with hard negative samples,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:40.211305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 29795170-3b4e-4f79-93a6-c04b55d7a881 · outbound

This paper cites SimCSE: Simple contrastive learn- ing of sentence embeddings,.

Clustering-based hard negative sampling for supervised contrastive speaker verification SimCSE: Simple contrastive learn- ing of sentence embeddings,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:40.020636Z

Source-reported events for the cited work

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Observation 93322a2e-1369-4397-a04e-e15409a74abd · outbound

This paper cites V oice biometrical match of twin and non-twin siblings,.

Clustering-based hard negative sampling for supervised contrastive speaker verification V oice biometrical match of twin and non-twin siblings,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:39.861878Z

Source-reported events for the cited work

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Observation d70390eb-c019-4621-8115-f95feac4ceba · outbound

This paper cites The JHU submission to VoxSRC-21: Track 3.

Clustering-based hard negative sampling for supervised contrastive speaker verification The JHU submission to VoxSRC-21: Track 3

Reference 27

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Source-reported events for the cited work

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Observation e5101e42-5373-46cd-87fd-653a1ba6b285 · outbound

This paper cites Improving dino-based self-supervised speaker verification with progressive cluster- aware training,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Improving dino-based self-supervised speaker verification with progressive cluster- aware training,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:39.696677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6a959d86-e03c-4094-9cb6-74bf5d96225e · outbound

This paper cites Cluster-guided unsupervised domain adaptation for deep speaker embedding,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Cluster-guided unsupervised domain adaptation for deep speaker embedding,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:39.533678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0b34d3f3-1ce6-45c8-bdd1-7036013c06a2 · outbound

This paper cites A triangle inequality for cosine similarity,.

Clustering-based hard negative sampling for supervised contrastive speaker verification A triangle inequality for cosine similarity,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:39.389387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7e1e8904-3753-4ba0-9668-81bcf5f2a72e · outbound

This paper cites Improved deep metric learning with multi-class n-pair loss objective,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Improved deep metric learning with multi-class n-pair loss objective,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:39.263283Z

Source-reported events for the cited work

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Observation 1d7de623-da73-4c75-9575-4635769afc4d · outbound

This paper cites V oxCeleb2: Deep Speaker Recognition,.

Clustering-based hard negative sampling for supervised contrastive speaker verification V oxCeleb2: Deep Speaker Recognition,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:38.670803Z

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Observation b3d47540-1e30-4f74-ac03-bcfa06f69831 · outbound

This paper cites Cn-celeb: a challenging chinese speaker recognition dataset,.

Clustering-based hard negative sampling for supervised contrastive speaker verification Cn-celeb: a challenging chinese speaker recognition dataset,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:39.105587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 876ffd37-7037-46df-9647-83dcdd509b33 · inbound