Paper Citation Record · LEDGER

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context

As of 21 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2505.17410.

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

pith.paper-citation-record.v1
2505.17410 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:17.012857Z

measured 49 of 49 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:13.932827Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:52:17.444518Z

Reference resolution

48 of 48 outbound references displayed

verified exact3
verified fuzzy33
unresolved10
parse uncertain0
malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4d24bd47-2cb6-4031-a6a5-cfe7cd539fad · outbound

This paper cites However, these systems often produce transcription errors, particularly due to back- ground noise, speaker accents, different speaker styles, and domain-specific terms.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context However, these systems often produce transcription errors, particularly due to back- ground noise, speaker accents, different speaker styles, and domain-specific terms

Reference 1

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Observation 91ca252e-487a-4868-b7a3-7bc9ed5eb2de · outbound

This paper cites LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:52:17.476275Z

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Observation 993e593f-e87e-42a3-b796-73234e06170d · outbound

This paper cites Provide 5 dif- ferent English sentences in various contexts that include the termw n, which is a medical term.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Provide 5 dif- ferent English sentences in various contexts that include the termw n, which is a medical term

Reference 3

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Observation cffcbd8e-4048-454c-8839-72a8799e83e3 · outbound

This paper cites Extract highly complex words for recognition, including tech- nical terms, names of people, and names of places.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Extract highly complex words for recognition, including tech- nical terms, names of people, and names of places

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:24.399692Z

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Observation 01a3b128-1f67-4d80-887c-c1f8d909fc4c · outbound

This paper cites WER / recall / precision.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context WER / recall / precision

Reference 5

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T14:52:23.920521Z

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Observation b532f260-1348-454a-84ae-87dd6b422167 · outbound

This paper cites We intro- duced a method for generating diverse synthetic data contain- ing rare words, combined with leveraging LLM-based simpli- fied phonemes to avoid over-correction.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context We intro- duced a method for generating diverse synthetic data contain- ing rare words, combined with leveraging LLM-based simpli- fied phonemes to avoid over-correction

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:23.664985Z

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Observation 6ee4c2ff-822d-4024-850e-4c57053cab8f · outbound

This paper cites Generative error correction for code-switching speech recognition using large language models.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Generative error correction for code-switching speech recognition using large language models

Reference 7

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Observation de7a2437-c6d9-4e8b-b2fb-3c67f14c7911 · outbound

This paper cites End-to-end speech recognition: A survey,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context End-to-end speech recognition: A survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:23.370909Z

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Observation 22569608-55d2-4ccf-93df-6fff182342d2 · outbound

This paper cites Non-autoregressive error correction for CTC-based ASR with phone-conditioned masked LM,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Non-autoregressive error correction for CTC-based ASR with phone-conditioned masked LM,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:23.078532Z

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Observation 184d4381-6f24-44e2-b64f-5cd5ed558453 · outbound

This paper cites Spelling error correction with soft-masked BERT,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Spelling error correction with soft-masked BERT,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:22.866447Z

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Observation d421d8f9-c1a2-40e5-8344-4578bd565974 · outbound

This paper cites Distilling the knowledge of BERT for sequence- to-sequence asr,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Distilling the knowledge of BERT for sequence- to-sequence asr,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:22.578202Z

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Observation e51162c4-7e39-4e68-8b9e-0740a157d488 · outbound

This paper cites Investigating asr error correction with large language model and multilingual 1-best hypotheses,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Investigating asr error correction with large language model and multilingual 1-best hypotheses,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:22.322370Z

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Observation 156d1063-698c-4e5e-b633-f0b49cde2128 · outbound

This paper cites Multi-stage Large Language Model Correction for Speech Recognition.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Multi-stage Large Language Model Correction for Speech Recognition

Reference 13

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Observation faca9e1c-138c-48ff-9141-c39587a8dbfd · outbound

This paper cites It’s never too late: Fusing acoustic informa- tion into large language models for automatic speech recognition,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context It’s never too late: Fusing acoustic informa- tion into large language models for automatic speech recognition,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:21.023709Z

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Observation 1c186345-1bfb-4904-b1ae-1ae0b84b2e55 · outbound

This paper cites Hyporadise: An open baseline for generative speech recognition with large language models,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Hyporadise: An open baseline for generative speech recognition with large language models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:22.057534Z

Source-reported events for the cited work

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Observation f6d3db82-291b-4d4f-a456-83de18d9d079 · outbound

This paper cites N-best T5: Ro- bust asr error correction using multiple input hypotheses and con- strained decoding space,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context N-best T5: Ro- bust asr error correction using multiple input hypotheses and con- strained decoding space,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:21.800238Z

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.

Observation 246ea9ee-e83b-4062-80ce-1ca491ca5116 · outbound

This paper cites Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correction.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correction

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:52:17.210785Z

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Observation 8a2932e0-ddcd-4b2c-a4a1-63eee354d47e · outbound

This paper cites Generative speech recognition error correction with large language models and task-activating prompting,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Generative speech recognition error correction with large language models and task-activating prompting,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:21.547628Z

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Observation 5eed9531-6a22-436f-a06d-065bb56208b6 · outbound

This paper cites Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation

Reference 19

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Observation b3610618-77f1-4406-8d0d-22f7b5e6004b · outbound

This paper cites Can large language models understand uncommon mean- ings of common words?.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Can large language models understand uncommon mean- ings of common words?

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:21.293013Z

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Observation a0891e43-9171-4a26-bc5d-86d69a0da2ce · outbound

This paper cites an unresolved cited work.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:52:19.811764Z

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Observation 08e7a852-e1e8-4c88-949c-afec5867f6b0 · outbound

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

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Can Generative Large Language Models Perform ASR Error Correction?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:15.308251Z

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Observation 29c8b0b8-79c9-4516-a2e8-a1fd2d1323f9 · outbound

This paper cites ChatGPT.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context ChatGPT

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:20.790004Z

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Observation 41951301-207f-4ea6-8a0c-5e2fd3c83b53 · outbound

This paper cites InterBiasing: Boost unseen word recognition through biasing intermediate predictions,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context InterBiasing: Boost unseen word recognition through biasing intermediate predictions,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:20.548198Z

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Observation d55680f5-fef3-46ee-9a82-fae2736ce453 · outbound

This paper cites ED-CEC: Improving rare word recognition using asr postprocessing based on error detection and context-aware error correction,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context ED-CEC: Improving rare word recognition using asr postprocessing based on error detection and context-aware error correction,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:20.299748Z

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Observation 8f4b167c-582f-4817-8285-edf80a1b2105 · outbound

This paper cites Entity resolution for noisy ASR transcripts,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Entity resolution for noisy ASR transcripts,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:20.008784Z

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Observation f654c550-c6dc-410a-a705-2e7fc683298c · outbound

This paper cites Retrieval Augmented Correction of Named Entity Speech Recognition Errors.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Retrieval Augmented Correction of Named Entity Speech Recognition Errors

Reference 27

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Observation ad7ffd62-da5e-4d71-9fc1-c6478b3723f6 · outbound

This paper cites EDGAR-CORPUS: Billions of tokens make the world go round,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context EDGAR-CORPUS: Billions of tokens make the world go round,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:18.844735Z

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This paper cites Simplified Japanese pho- netic alphabet as a tool for Japanese course design,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Simplified Japanese pho- netic alphabet as a tool for Japanese course design,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:19.541837Z

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Observation c5e89296-b362-4ae2-8c88-b4ee5d4bd11e · outbound

This paper cites Parallel Tacotron 2: A non-autoregressive neural TTS model with differentiable duration modeling,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Parallel Tacotron 2: A non-autoregressive neural TTS model with differentiable duration modeling,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:19.397028Z

Source-reported events for the cited work

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Observation 37f60a0e-35d3-494e-9125-1e270e6525b8 · outbound

This paper cites Data driven grapheme-to-phoneme representations for a lexicon-free text-to- speech,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Data driven grapheme-to-phoneme representations for a lexicon-free text-to- speech,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:19.269718Z

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This paper cites LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study

Reference 32

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This paper cites Common V oice: A massively-multilingual speech corpus,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Common V oice: A massively-multilingual speech corpus,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:19.154092Z

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Observation 7df3f131-8fad-4a02-bf8d-601c3f339df8 · outbound

This paper cites Lib- rispeech: an asr corpus based on public domain audio books,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Lib- rispeech: an asr corpus based on public domain audio books,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:18.983387Z

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Observation dabf2139-1bb1-4b09-b95e-59c385e2b1ae · outbound

This paper cites JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis

Reference 35

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Observation cb368ccb-23c1-47bb-881d-685972b45740 · outbound

This paper cites Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:18.740893Z

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Observation 60851213-6dbb-4b3d-877f-3b2dc9f9fe15 · outbound

This paper cites CSTR VCTK corpus: English multi-speaker corpus for CSTR voice cloning toolkit,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context CSTR VCTK corpus: English multi-speaker corpus for CSTR voice cloning toolkit,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:18.632183Z

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Observation a086fcb1-d369-49f7-ba5c-8806ea2320e8 · outbound

This paper cites The Kaldi speech recognition toolkit,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context The Kaldi speech recognition toolkit,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:18.504588Z

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Observation c853ed04-4e42-4bbd-950f-6b3ae73695fd · outbound

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LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:52:24.197358Z

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Observation e98b2d0f-3f2f-41d6-b26a-edfe45142e43 · outbound

This paper cites Real- mednlp: Overview of real document-based medical natural lan- guage processing task,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Real- mednlp: Overview of real document-based medical natural lan- guage processing task,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:18.369160Z

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Observation b7501c1f-dd39-4816-9344-aa9aacd8daad · outbound

This paper cites FastSpeech 2: Fast and High-Quality End-to-End Text to Speech.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context FastSpeech 2: Fast and High-Quality End-to-End Text to Speech

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.526730Z

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Observation bbf7ebb2-9a31-4997-9194-800a88d13d48 · outbound

This paper cites Hifi-gan: Generative adversarial net- works for efficient and high fidelity speech synthesis,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Hifi-gan: Generative adversarial net- works for efficient and high fidelity speech synthesis,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:16.580311Z

Source-reported events for the cited work

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Observation ea3f612c-1819-467f-a683-81f43b96c07b · outbound

This paper cites Contextualized streaming end-to-end speech recognition with trie-based deep bi- asing and shallow fusion,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Contextualized streaming end-to-end speech recognition with trie-based deep bi- asing and shallow fusion,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:18.224776Z

Source-reported events for the cited work

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Observation 0e1fb37d-7519-4959-b72c-bc470f981128 · outbound

This paper cites AI Speech: Azure AI Services,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context AI Speech: Azure AI Services,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:18.111790Z

Source-reported events for the cited work

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Observation e3a84551-c9bf-407b-a2d7-6dba61983b52 · outbound

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

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Robust speech recognition via large-scale weak supervision,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:17.950593Z

Source-reported events for the cited work

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Observation f85a7a7b-43f6-42cc-8d9b-adc55f69975d · outbound

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

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context LoRA: Low-rank adaptation of large lan- guage models,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:17.831192Z

Source-reported events for the cited work

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Observation 0417437a-9e88-4b7c-8ad8-03e181a0d2a7 · outbound

This paper cites Spell my name: Keyword boosted speech recognition,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Spell my name: Keyword boosted speech recognition,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:17.698330Z

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.

Observation e8d156ea-18a9-4c75-aa16-8eae53b32eb6 · outbound

This paper cites Distribution of homonyms in Japanese text,.

LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context Distribution of homonyms in Japanese text,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:52:17.577050Z

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

Pith citing papers

Observation 91ca252e-487a-4868-b7a3-7bc9ed5eb2de · inbound