Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Training
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2207.00659.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:53:01.220124Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T13:33:28.393030Z
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 5889866f-8192-4647-8995-6f73764f0667 · inbound
Fotheidil: an Automatic Transcription System for the Irish Language cites this paper.
Fotheidil: an Automatic Transcription System for the Irish Language Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Training
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:53:01.220124Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 63544c9f-718c-4d4c-b37e-a7d76dd4d318 · inbound
Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages cites this paper.
Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Training
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:35:29.405393Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 03faa95f-8555-41db-a45f-9c11d303cf0e · inbound
Building Community-Centred NLP Resources for Puno Quechua cites this paper.
Building Community-Centred NLP Resources for Puno Quechua Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Training
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T13:33:28.394834Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.