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Paper Citation Record · LEDGER

Customizing Speech Recognition Model with Large Language Model Feedback

As of 8 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2506.11091.

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

pith.paper-citation-record.v1
2506.11091 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

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measured 37 of 37 standing notices

One-hop event checks from named stored sources.

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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

37 of 37 outbound references displayed

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External citation measurements

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Outbound references

Observation bb1b7e32-3d60-45bc-bb1e-9ee6622d7a4b · outbound

This paper cites Recent advances in end-to-end automatic speech recog- nition,.

Customizing Speech Recognition Model with Large Language Model Feedback Recent advances in end-to-end automatic speech recog- nition,

Reference 1

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Observation 86831cb6-9b16-426b-94b6-b3567e8811e4 · outbound

This paper cites Adaptation algorithms for neural network-based speech recognition: An overview,.

Customizing Speech Recognition Model with Large Language Model Feedback Adaptation algorithms for neural network-based speech recognition: An overview,

Reference 2

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Observation 529c4b05-6ec9-47aa-b11d-c81b98f56829 · outbound

This paper cites Self-taught learning: transfer learning from unlabeled data,.

Customizing Speech Recognition Model with Large Language Model Feedback Self-taught learning: transfer learning from unlabeled data,

Reference 3

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Observation 9b7045eb-1164-42a8-9371-379abd366742 · outbound

This paper cites Confidence estimation for attention-based sequence-to- sequence models for speech recognition,.

Customizing Speech Recognition Model with Large Language Model Feedback Confidence estimation for attention-based sequence-to- sequence models for speech recognition,

Reference 4

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Observation 86368eb2-c7cd-410d-b44d-66e316a2869d · outbound

This paper cites GPT-4 Technical Report.

Customizing Speech Recognition Model with Large Language Model Feedback GPT-4 Technical Report

Reference 5

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Observation 7eb71aa5-2aa7-4a64-9eaf-f85e6d59ad3c · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Customizing Speech Recognition Model with Large Language Model Feedback DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 6

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Observation 7e08c232-b1fd-4a92-986a-15770e029242 · outbound

This paper cites Phi-4 Technical Report.

Customizing Speech Recognition Model with Large Language Model Feedback Phi-4 Technical Report

Reference 7

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Observation 1e48e473-20c1-4f4a-9ac0-d5b31b7ef0ce · outbound

This paper cites Massively multilingual shal- low fusion with large language models,.

Customizing Speech Recognition Model with Large Language Model Feedback Massively multilingual shal- low fusion with large language models,

Reference 8

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Observation c62d28d2-74fe-4c6b-a316-3d15f22ee6cf · outbound

This paper cites Prompting large language models for zero-shot domain adaptation in speech recognition,.

Customizing Speech Recognition Model with Large Language Model Feedback Prompting large language models for zero-shot domain adaptation in speech recognition,

Reference 9

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Observation 44882cc1-91f0-4e3b-90ec-1d898921b02b · outbound

This paper cites Large- scale language model rescoring on long-form data,.

Customizing Speech Recognition Model with Large Language Model Feedback Large- scale language model rescoring on long-form data,

Reference 10

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Observation 6dbdb574-c1e9-4537-a403-547288ec51e7 · outbound

This paper cites Applying LLMs for Rescoring N-best ASR Hypotheses of Casual Conversations: Effects of Domain Adaptation and Context Carry-over.

Customizing Speech Recognition Model with Large Language Model Feedback Applying LLMs for Rescoring N-best ASR Hypotheses of Casual Conversations: Effects of Domain Adaptation and Context Carry-over

Reference 11

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Observation c432666d-971e-4d00-b228-9cd248959345 · outbound

This paper cites Can Generative Large Language Models Perform ASR Error Correction?.

Customizing Speech Recognition Model with Large Language Model Feedback Can Generative Large Language Models Perform ASR Error Correction?

Reference 12

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Observation 0a299357-0e87-473f-b7f1-eed56817a4fd · outbound

This paper cites Denoising lm: Pushing the limits of error correction models for speech recognition,.

Customizing Speech Recognition Model with Large Language Model Feedback Denoising lm: Pushing the limits of error correction models for speech recognition,

Reference 13

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Observation 3d2994fa-2781-4bee-95f5-10bd8e34c2ee · outbound

This paper cites Asr error correction using large language models,.

Customizing Speech Recognition Model with Large Language Model Feedback Asr error correction using large language models,

Reference 14

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Source-reported events for the cited work

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Observation 5d048594-1648-40b9-aefa-cd1829d0ea38 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Customizing Speech Recognition Model with Large Language Model Feedback Constitutional AI: Harmlessness from AI Feedback

Reference 15

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Observation 9e272212-0a67-482e-b5a1-9831005a09d1 · outbound

This paper cites Direct Language Model Alignment from Online AI Feedback.

Customizing Speech Recognition Model with Large Language Model Feedback Direct Language Model Alignment from Online AI Feedback

Reference 16

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Observation a3e38b82-6e67-402f-9760-cb58cccbffb0 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model,.

Customizing Speech Recognition Model with Large Language Model Feedback Direct preference optimization: Your language model is secretly a reward model,

Reference 17

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Observation 5b3ec5fa-61e2-49b3-9e52-c6e6be981ce6 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Customizing Speech Recognition Model with Large Language Model Feedback DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 18

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Observation f2243a19-6600-4acf-834e-95e4d29fc1d2 · outbound

This paper cites Boost- ing cross-domain speech recognition with self-supervision,.

Customizing Speech Recognition Model with Large Language Model Feedback Boost- ing cross-domain speech recognition with self-supervision,

Reference 19

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Observation 89f52bff-4dff-417e-a769-e11174fbb605 · outbound

This paper cites An unsupervised deep domain adaptation approach for robust speech recognition,.

Customizing Speech Recognition Model with Large Language Model Feedback An unsupervised deep domain adaptation approach for robust speech recognition,

Reference 20

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Observation d0769e7a-3790-488a-b0c5-0d29de53fe66 · outbound

This paper cites A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation.

Customizing Speech Recognition Model with Large Language Model Feedback A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation

Reference 21

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Observation 27a9c7cb-c3b0-4575-868e-c7b2f043da2f · outbound

This paper cites Toward domain-invariant speech recognition via large scale training,.

Customizing Speech Recognition Model with Large Language Model Feedback Toward domain-invariant speech recognition via large scale training,

Reference 22

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Observation c31ab385-546b-4e9f-a722-046cb75afce2 · outbound

This paper cites Large-Scale Domain Adaptation via Teacher-Student Learning.

Customizing Speech Recognition Model with Large Language Model Feedback Large-Scale Domain Adaptation via Teacher-Student Learning

Reference 23

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Observation 7ae69405-dbd0-4c63-9ccb-1b56580e8abc · outbound

This paper cites Conditional teacher-student learning,.

Customizing Speech Recognition Model with Large Language Model Feedback Conditional teacher-student learning,

Reference 24

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Observation 1eb3666e-3529-4e3b-970b-e7a5317639f0 · outbound

This paper cites Unsupervised domain adaptation for speech recognition via uncertainty driven self-training,.

Customizing Speech Recognition Model with Large Language Model Feedback Unsupervised domain adaptation for speech recognition via uncertainty driven self-training,

Reference 25

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Source-reported events for the cited work

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Observation 099ba880-52ba-493d-a564-ca50e9cac7b8 · outbound

This paper cites Large-scale asr domain adaptation using self-and semi-supervised learning,.

Customizing Speech Recognition Model with Large Language Model Feedback Large-scale asr domain adaptation using self-and semi-supervised learning,

Reference 26

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Observation dba27fed-f028-4870-8bb4-654d97c8291d · outbound

This paper cites Improving pseudo-label training for end-to-end speech recognition using gradient mask,.

Customizing Speech Recognition Model with Large Language Model Feedback Improving pseudo-label training for end-to-end speech recognition using gradient mask,

Reference 27

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Observation cf8b171c-5cbf-4ccb-859f-f25fc9b68ab7 · outbound

This paper cites Self-taught recognizer: Toward unsupervised adaptation for speech foundation models,.

Customizing Speech Recognition Model with Large Language Model Feedback Self-taught recognizer: Toward unsupervised adaptation for speech foundation models,

Reference 28

Resolution
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Source-reported events for the cited work

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Observation 356d1036-38e6-4a68-a7f7-f3ae033650fb · outbound

This paper cites Partitioning attention weight: Mitigat- ing adverse effect of incorrect pseudo-labels for self-supervised asr,.

Customizing Speech Recognition Model with Large Language Model Feedback Partitioning attention weight: Mitigat- ing adverse effect of incorrect pseudo-labels for self-supervised asr,

Reference 29

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Source-reported events for the cited work

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Observation f32eb56d-06b1-4e60-9b0d-c4153d20bf92 · outbound

This paper cites Self-critical sequence training for automatic speech recognition,.

Customizing Speech Recognition Model with Large Language Model Feedback Self-critical sequence training for automatic speech recognition,

Reference 30

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Source-reported events for the cited work

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Observation afb642ee-43a6-4747-b897-dc0677e8739a · outbound

This paper cites Sequence-to-sequence asr optimization via reinforcement learning,.

Customizing Speech Recognition Model with Large Language Model Feedback Sequence-to-sequence asr optimization via reinforcement learning,

Reference 31

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Source-reported events for the cited work

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Observation f29fe0b4-8ed5-46e1-bfe5-28abc0f0b9ef · outbound

This paper cites Reinforcement learning of speech recognition system based on policy gradient and hypothesis selection,.

Customizing Speech Recognition Model with Large Language Model Feedback Reinforcement learning of speech recognition system based on policy gradient and hypothesis selection,

Reference 32

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Source-reported events for the cited work

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Observation 93597800-d7a9-4053-93fb-d358917a779b · outbound

This paper cites A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce.

Customizing Speech Recognition Model with Large Language Model Feedback A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reference 33

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Observation fc6e5a19-39b2-409b-9859-48adb058f373 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Customizing Speech Recognition Model with Large Language Model Feedback Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 34

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Observation 66579e4f-bace-4736-81b5-770d26a6efa9 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Customizing Speech Recognition Model with Large Language Model Feedback Proximal Policy Optimization Algorithms

Reference 35

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Source-reported events for the cited work

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Observation ee3b88f4-e140-46af-8de4-3d0929d34772 · outbound

This paper cites A Call for Clarity in Beam Search: How It Works and When It Stops.

Customizing Speech Recognition Model with Large Language Model Feedback A Call for Clarity in Beam Search: How It Works and When It Stops

Reference 36

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local_arxiv, observed 2026-08-07T10:23:21.583523Z

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=2026-08-07T10:23:21.352870Z digest=sha256:67bfef6a41afb6354b095808c913eaaa61b9759a6673fdbbb1ae3d6a1f82cd23

Observation 24827675-146a-4610-84eb-654701662608 · outbound

This paper cites SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition.

Customizing Speech Recognition Model with Large Language Model Feedback SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T10:23:21.424241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:23:21.424241Z digest=sha256:920cecc027acf0d8c1dbe2faae928746a0389c55831b5069f42440ad18bd1a02

Pith citing papers

No inbound Pith citation observations are available.