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Paper Citation Record · LEDGER

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling

As of 18 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2412.16474.

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

pith.paper-citation-record.v1
2412.16474 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:36:28.361661Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

  • verified exact1
  • verified fuzzy7
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c0ceff4f-77c2-4749-9df1-12b3ed784a1e · outbound

This paper cites Deep speech 2: End-to-end speech recognition in english and mandarin,.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling Deep speech 2: End-to-end speech recognition in english and mandarin,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:36:28.548482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T10:36:28.303947Z digest=sha256:4282c0fd5f334f91a053aeaea28ade254bf3f452cafc40d3b0b18c75237d9e8e

Observation b5065505-59a6-4219-aac6-42ce58676d9e · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations,.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T10:36:28.309762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:36:28.309762Z digest=sha256:4cca7dfe6b3e48fad16ebc5ea6566dce52c4333b443b41d35c0a278e8239a631

Observation d184aa29-9d98-4717-a2a5-eb6d728d0bb3 · outbound

This paper cites Transformer transducer: A streamable speech recognition model with transformer encoders and rnn-t loss,.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling Transformer transducer: A streamable speech recognition model with transformer encoders and rnn-t loss,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:36:28.526914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T10:36:28.313909Z digest=sha256:cc316349d5e17ae292d481aac73d5d64cd1945bc6518452f3a5105b8c5bfb272

Observation c95ed1ce-3a6f-454b-8fa5-1459de0b1645 · outbound

This paper cites Multilingual speech recognition with a single end-to-end model,.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling Multilingual speech recognition with a single end-to-end model,

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-11T10:36:28.318103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:36:28.318103Z digest=sha256:bef0ec616c611017bb3a2436febc84c0d91ddfd73f5d22aeb9c21c843f535ed9

Observation 9791f950-410c-492a-8fe2-06f96953880b · outbound

This paper cites Un- supervised cross-lingual representation learning for speech recognition,.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling Un- supervised cross-lingual representation learning for speech recognition,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:36:28.503878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T10:36:28.322089Z digest=sha256:b416b33d8b224c93272b47770d471bc64c01ae4fde48b3b1cd2855acc99c79a0

Observation a8dcedf0-ba17-4e6a-88a7-b748c1e48a55 · outbound

This paper cites Xls-r: Self-supervised cross-lingual speech representation learning at scale,.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling Xls-r: Self-supervised cross-lingual speech representation learning at scale,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:36:28.490012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T10:36:28.326511Z digest=sha256:85f76af8ad3664ce2462cf84d725c767547fcc501235b199ce35f208ad93cee0

Observation 22b310b5-aa0b-416f-8e63-8949370ca151 · outbound

This paper cites Robust speech recognition via large-scale weak supervi- sion,.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling Robust speech recognition via large-scale weak supervi- sion,

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T10:36:28.330707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:36:28.330707Z digest=sha256:ede1dff1ed565c931799404003338fcaa23dd171b50fe80f4396007bea1ab376

Observation 90027c9a-08d5-4b40-9293-ed7f1c15c24a · outbound

This paper cites Yang, K.-P.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling Yang, K.-P

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T10:36:28.334366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:36:28.334366Z digest=sha256:38566380db230d2531f18a5a34e4d5893760e58137a18ef02fffe1b61d1b9570

Observation 7cbb765a-1535-4394-bef2-2d101d477a90 · outbound

This paper cites Do Prompts Really Prompt? Exploring the Prompt Understanding Capability of Whisper.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling Do Prompts Really Prompt? Exploring the Prompt Understanding Capability of Whisper

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-11T10:36:28.412451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T10:36:28.338327Z digest=sha256:e70000b2c89430776ab65c2ce42ed3b208279dcc5d43b2cf05c89968dd14f1c6

Observation 3154b719-9713-4b30-8d71-926c95d49e66 · outbound

This paper cites Prompting the hidden talent of web-scale speech models for zero-shot task generalization,.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling Prompting the hidden talent of web-scale speech models for zero-shot task generalization,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:36:28.458867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T10:36:28.343499Z digest=sha256:7f87001cfdb66faa5422bdffb90c9d0c22cd9821919e42a0ed5d6ca28b2c04ac

Observation 2efee83b-cf0c-43e9-a26a-3d6e11566256 · outbound

This paper cites Zero- shot domain-sensitive speech recognition with prompt-conditioning fine- tuning,.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling Zero- shot domain-sensitive speech recognition with prompt-conditioning fine- tuning,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:36:28.446300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T10:36:28.348209Z digest=sha256:0b6eb30ac9bb87a7302d0cbb2bfefe0404d9cbfccfa3d95c076eb72f099947c1

Observation 7024d41c-da7d-4054-bf7e-496e9c231b62 · outbound

This paper cites Improving Whisper's Recognition Performance for Under-Represented Language Kazakh Leveraging Unpaired Speech and Text.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling Improving Whisper's Recognition Performance for Under-Represented Language Kazakh Leveraging Unpaired Speech and Text

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T10:36:28.352134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:36:28.352134Z digest=sha256:419696291c54b909cf4b4071d0f091544a5c45c9e0c10fc4389f2927f573cdd8

Observation dcf0c166-97c9-49be-9df9-6aa4200c2fde · outbound

This paper cites ML- SUPERB: Multilingual Speech Universal PERformance Benchmark,.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling ML- SUPERB: Multilingual Speech Universal PERformance Benchmark,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T10:36:28.434433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T10:36:28.357639Z digest=sha256:388ed279dc1284fcdc7f4e346ea591e9c85d984d4c356869005a96279a417c01

Observation 2c510af8-a774-4878-8ef1-8f615c9871b4 · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling LoRA: Low-rank adaptation of large language models,

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T10:36:28.361661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:36:28.361661Z digest=sha256:dca32238291d24d21e8f82bacf758565a843cb190726b52a530c07053ffcd4b1

Pith citing papers

No inbound Pith citation observations are available.