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Paper Citation Record · LEDGER

Towards Naturalistic Voice Conversion: NaturalVoices Dataset with an Automatic Processing Pipeline

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.04494.

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

pith.paper-citation-record.v1
2406.04494 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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:50:21.277794Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:18:08.095963Z

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 40cd74ab-76c5-4f60-83bc-409f9ad5c4fe · inbound

Training-Free Multi-Step Audio Source Separation cites this paper.

Training-Free Multi-Step Audio Source Separation Towards Naturalistic Voice Conversion: NaturalVoices Dataset with an Automatic Processing Pipeline

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:18:08.137067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:18:05.577321Z digest=sha256:c50cc014173e9f067742bfcbfe708248eca62e80e5e206913e7269cea70f29bf

Observation a08dfd0e-dff9-4ded-a006-3a34fb9c487f · inbound

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks cites this paper.

AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks Towards Naturalistic Voice Conversion: NaturalVoices Dataset with an Automatic Processing Pipeline

Reference 131

Resolution
unresolved
no resolver link, observed 2026-08-08T11:50:21.277794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T11:50:21.277794Z digest=sha256:ed8edc68bc069d35f0431009164ac4137cb35ac4f4ff3d343cfacb2164e69293