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Paper Citation Record · LEDGER

Towards Robust Speech Representation Learning for Thousands of Languages

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.00837.

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

pith.paper-citation-record.v1
2407.00837 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:58:56.363483Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T21:33:28.485250Z

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 bd2ac121-e8ba-4c3e-8070-b2b4c523041f · inbound

AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge cites this paper.

AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge Towards Robust Speech Representation Learning for Thousands of Languages

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-23T21:33:28.496612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-23T21:32:19.748956Z digest=sha256:a29abdd9e1523400b2dca8585b459346f413c46b2b87ff44b92de22d9786dea1

Observation bf85f6d3-9313-4d5c-9dff-88dffc891119 · inbound

EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion cites this paper.

EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion Towards Robust Speech Representation Learning for Thousands of Languages

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T14:58:56.363483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:58:56.363483Z digest=sha256:d6b00cefe8c72bf2c4ef567932709fc592c42a882c8e4f65f54acbdab4e7c9ea

Observation 9b65f5b2-3f11-49c6-800e-ef63fd2fe423 · inbound

From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition cites this paper.

From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition Towards Robust Speech Representation Learning for Thousands of Languages

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T14:55:54.752647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:54.752647Z digest=sha256:3c64e9974bca83cf67f0c0222372cd59f456810b5b0a4cbbe459415ba4d08157

Observation e7ef973d-14e2-43c9-964e-96bbab9d46dc · inbound

Chain-of-Thought Training for Open E2E Spoken Dialogue Systems cites this paper.

Chain-of-Thought Training for Open E2E Spoken Dialogue Systems Towards Robust Speech Representation Learning for Thousands of Languages

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T12:04:33.822916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:04:33.822916Z digest=sha256:87cf2fc94cca69831c612b106fa22eec59d95baa9c1c8668ca0c71c8144eb53a

Observation bb07765b-f138-44cb-a987-e0b13c1789ee · inbound

SenSE: Semantic-Aware High-Fidelity Universal Speech Enhancement cites this paper.

SenSE: Semantic-Aware High-Fidelity Universal Speech Enhancement Towards Robust Speech Representation Learning for Thousands of Languages

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-18T12:36:22.284843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T12:35:19.023045Z digest=sha256:3b3ecf8866d7353c13491511ce2b7f81fa30d4885e78d914669bb220f4ae61e2