Paper Citation Record · LEDGER

Can Contextual Biasing Remain Effective with Whisper and GPT-2?

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2306.01942.

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

pith.paper-citation-record.v1
2306.01942 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:56:53.787650Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T02:49:24.368466Z

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 ce19127e-5d87-4e38-bfc7-27aa858b046b · inbound

Improving Contextual ASR via Multi-grained Fusion with Large Language Models cites this paper.

Improving Contextual ASR via Multi-grained Fusion with Large Language Models Can Contextual Biasing Remain Effective with Whisper and GPT-2?

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:53.787650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cb56a5a-dd5d-4345-ad31-cc836b411c58 · inbound

Efficient Trie-based Biasing using K-step Prediction for Rare Word Recognition cites this paper.

Efficient Trie-based Biasing using K-step Prediction for Rare Word Recognition Can Contextual Biasing Remain Effective with Whisper and GPT-2?

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T19:36:44.130176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07c47d5f-b1a8-45bf-bdb3-776d4422ebde · inbound

Improving Synthetic Data Training for Contextual Biasing Models with a Keyword-Aware Cost Function cites this paper.

Improving Synthetic Data Training for Contextual Biasing Models with a Keyword-Aware Cost Function Can Contextual Biasing Remain Effective with Whisper and GPT-2?

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T19:35:50.629682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac12e919-5485-4c5a-b326-af77d1ded04c · inbound

IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages cites this paper.

IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages Can Contextual Biasing Remain Effective with Whisper and GPT-2?

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-04T02:49:24.370422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.