Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2010.10504.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:14:10.731223Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T01:59:26.690851Z
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 f88df30a-06c3-407b-bcb2-d572eb1513ff · inbound
Vision Transformers Need Registers cites this paper.
Vision Transformers Need Registers Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
Reference 135
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T09:41:38.220021Z
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.
Observation 64f6980f-7ad9-4342-a4ba-947767239bc0 · inbound
OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction cites this paper.
OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:14:10.731223Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a93d609a-ea21-41e6-ba55-b07867f875dc · inbound
Contextualized Token Discrimination for Speech Search Query Correction cites this paper.
Contextualized Token Discrimination for Speech Search Query Correction Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
Reference 66
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T10:16:12.143922Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3b1ee6d2-3d86-4470-8032-10b3e767f42c · inbound
ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition cites this paper.
ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T12:00:58.068320Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bbe9dfa5-fcce-47f2-9ee4-c802b015bb37 · inbound
Adopting State-of-the-Art Pretrained Audio Representations for Music Recommender Systems cites this paper.
Adopting State-of-the-Art Pretrained Audio Representations for Music Recommender Systems Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
Reference 104
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:01:13.295845Z
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.
Observation 680e46ee-f064-49bf-941f-5321f69532b5 · inbound
ViP-VL: Vietnamese Self-supervised Speech Pretraining Model with Vector-Quantization Learning cites this paper.
ViP-VL: Vietnamese Self-supervised Speech Pretraining Model with Vector-Quantization Learning Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
Reference 28
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T07:17:44.654564Z
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.
Observation a71b1d6b-c4c3-4976-b59f-32a318aad516 · inbound
Responsible ASR: Overcoming Challenges of Foundational Models in Narrow-Band and Low-Resource Settings cites this paper.
Responsible ASR: Overcoming Challenges of Foundational Models in Narrow-Band and Low-Resource Settings Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
Reference 32
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T01:59:26.693110Z
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.