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Paper Citation Record · LEDGER

Enrolment-based personalisation for improving individual-level fairness in speech emotion recognition

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.06665.

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

pith.paper-citation-record.v1
2406.06665 v1

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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:12:05.356971Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T11:38:36.952883Z

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 7e4e5bf6-a431-4c35-9614-45cb760b129c · inbound

Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment cites this paper.

Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment Enrolment-based personalisation for improving individual-level fairness in speech emotion recognition

Reference 74

Resolution
verified exact
local_arxiv, observed 2026-08-11T11:38:36.956630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-11T11:38:35.584749Z digest=sha256:08a496bf54dabdfb09c8f0ba339926bb21b4fdbfce8ce97e081bdba549ad5242

Observation 5b652fce-3e77-4365-a1bf-d818aa12240a · inbound

Affect Models Have Weak Generalizability to Atypical Speech cites this paper.

Affect Models Have Weak Generalizability to Atypical Speech Enrolment-based personalisation for improving individual-level fairness in speech emotion recognition

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-16T11:12:05.356971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:12:05.356971Z digest=sha256:e5dd422599aa17ec50ba63c079868df3ee0c6393a2f6e726b99755039ba11c34