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Paper Citation Record · LEDGER

Leveraging Self-Supervised Learning for Speaker Diarization

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.09408.

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

pith.paper-citation-record.v1
2409.09408 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:23:27.854046Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:09:25.913880Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1dde31ec-7d47-4e3d-a560-aa9f538fbda4 · inbound

Overlap-Adaptive Hybrid Speaker Diarization and ASR-Aware Observation Addition for MISP 2025 Challenge cites this paper.

Overlap-Adaptive Hybrid Speaker Diarization and ASR-Aware Observation Addition for MISP 2025 Challenge Leveraging Self-Supervised Learning for Speaker Diarization

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T13:23:27.854046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:23:27.854046Z digest=sha256:9e29aa6171923583a46ec49e3ba21cccf6a1b160cbaa44bbe1ac8f8fe6e9241b

Observation 85f150da-1f20-49b0-9c93-7c6370729460 · inbound

Dissecting the Segmentation Model of End-to-End Diarization with Vector Clustering cites this paper.

Dissecting the Segmentation Model of End-to-End Diarization with Vector Clustering Leveraging Self-Supervised Learning for Speaker Diarization

Reference 15

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:09:26.063030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:09:16.963379Z digest=sha256:59262019bf05da6d10f2b8190973d37b8bf0747922602a7226ac7993fd0b6c69