Paper Citation Record · LEDGER

LLM-based phoneme-to-grapheme for phoneme-based speech recognition

As of 9 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2506.04711.

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

pith.paper-citation-record.v1
2506.04711 v1

Coverage vector

measured 36 of 36 reference resolution

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measured 37 of 37 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:40:54.228445Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T10:40:57.090496Z

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation 44f88352-e306-4159-8cb3-6ec920acf1ef · outbound

This paper cites LLM-based phoneme-to-grapheme for phoneme-based speech recognition.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition LLM-based phoneme-to-grapheme for phoneme-based speech recognition

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T10:40:57.136403Z

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Observation a573a24d-fe68-4560-a6b7-481b018f1608 · outbound

This paper cites These prior works share a similar motivation with ours that phoneme-based supervision is advantageous for multilingual acoustic representation learning.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition These prior works share a similar motivation with ours that phoneme-based supervision is advantageous for multilingual acoustic representation learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:41:02.272363Z

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Observation 0aa21601-ec94-4d04-b686-4cdeb58a11a5 · outbound

This paper cites Whistle Subword FT.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Whistle Subword FT

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:41:02.130411Z

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Observation fe4eae7e-b119-4f76-8dc1-2c2d98b73e36 · outbound

This paper cites Dataset Experiments are conducted on the CommonV oice (CV) dataset [22], version 11.0 (released September 2022).

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Dataset Experiments are conducted on the CommonV oice (CV) dataset [22], version 11.0 (released September 2022)

Reference 4

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Observation 518bab98-30ee-46a9-9244-1dad95e70a8d · outbound

This paper cites Results The main results are shown in Table 1.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Results The main results are shown in Table 1

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:41:01.497256Z

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Observation 322c6be4-fd5f-4d17-9a81-7d54a565a629 · outbound

This paper cites Ablation results are shown in Table 3 and 4.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Ablation results are shown in Table 3 and 4

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:41:01.743809Z

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Observation 07e99b7b-eb8c-428f-a58f-129ba069d822 · outbound

This paper cites Whistle: Data-efficient multilingual and crosslingual speech recognition via weakly phonetic supervision,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Whistle: Data-efficient multilingual and crosslingual speech recognition via weakly phonetic supervision,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:59.265206Z

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LLM-based phoneme-to-grapheme for phoneme-based speech recognition Unresolved cited work

Reference 8

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Observation b6887d5c-f241-413d-8536-cd34708fa227 · outbound

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LLM-based phoneme-to-grapheme for phoneme-based speech recognition Unresolved cited work

Reference 9

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Observation 9f1fb3d9-9cbd-4cd2-b548-a98475417433 · outbound

This paper cites Univer- sal phone recognition with a multilingual allophone system,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Univer- sal phone recognition with a multilingual allophone system,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:41:00.716263Z

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Observation 344a6636-2a92-4cee-946c-53848d45b45e · outbound

This paper cites Multilingual and crosslin- gual speech recognition using phonological-vector based phone embeddings,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Multilingual and crosslin- gual speech recognition using phonological-vector based phone embeddings,

Reference 11

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Observation b2f38fe5-0536-447f-beda-7aaf45fbc399 · outbound

This paper cites Massively multilingual asr on 70 lan- guages: Tokenization, architecture, and generalization capabili- ties,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Massively multilingual asr on 70 lan- guages: Tokenization, architecture, and generalization capabili- ties,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:41:00.190375Z

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Observation 2a843bc8-f315-4b02-aa5d-91abca8935d3 · outbound

This paper cites Simple and effective zero-shot cross-lingual phoneme recognition,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Simple and effective zero-shot cross-lingual phoneme recognition,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:59.923779Z

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Observation 131258a3-cf27-483e-97c4-f4050961da28 · outbound

This paper cites Investigation into phone-based subword units for multilingual end-to-end speech recognition,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Investigation into phone-based subword units for multilingual end-to-end speech recognition,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:59.646050Z

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Observation 7d28df0a-ff17-4e0c-a7cc-617f5734b36e · outbound

This paper cites Allophant: Cross- lingual phoneme recognition with articulatory attributes,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Allophant: Cross- lingual phoneme recognition with articulatory attributes,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:59.458072Z

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Observation 637e4b82-b995-47f0-a165-7a9c85592023 · outbound

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

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Can Generative Large Language Models Perform ASR Error Correction?

Reference 16

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Observation 4f4f9b58-f4d6-4d19-87f3-eea40ecb7af5 · outbound

This paper cites Low-resourced speech recognition for iu mien language via weakly-supervised phoneme-based multilingual pretraining,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Low-resourced speech recognition for iu mien language via weakly-supervised phoneme-based multilingual pretraining,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:59.067545Z

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Observation fa43ec14-2342-4fd8-bb0e-a11a855bc363 · outbound

This paper cites Speech recognition with weighted finite-state transducers,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Speech recognition with weighted finite-state transducers,

Reference 18

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Observation 0a116e0b-7395-438a-824c-3c6da9a57a0b · outbound

This paper cites mt5: A massively multilingual pre-trained text-to-text transformer,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition mt5: A massively multilingual pre-trained text-to-text transformer,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:58.602631Z

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Observation da6f7d64-eb3f-4b0a-a473-4d008a6af523 · outbound

This paper cites Whistle Phoneme FT.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Whistle Phoneme FT

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:41:01.876652Z

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Observation ed7ab7be-32c6-4773-811c-e9790c4531f7 · outbound

This paper cites GPT-4 Technical Report.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition GPT-4 Technical Report

Reference 21

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Observation 32a86eec-6efb-4a82-9142-89bacfc4179e · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition LLaMA: Open and Efficient Foundation Language Models

Reference 22

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Observation bb6c0b56-467c-40e5-97dc-ba5c8c034525 · outbound

This paper cites TranUSR: Phoneme-to-word transcoder based unified speech representa- tion learning for cross-lingual speech recognition,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition TranUSR: Phoneme-to-word transcoder based unified speech representa- tion learning for cross-lingual speech recognition,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:58.374557Z

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Observation 8437556f-04a6-4b7a-951a-8cd73dc22870 · outbound

This paper cites Optimizing two-pass cross- lingual transfer learning: Phoneme recognition and phoneme to grapheme translation,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Optimizing two-pass cross- lingual transfer learning: Phoneme recognition and phoneme to grapheme translation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:58.176816Z

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Observation 22609ee5-e927-45e6-bb4f-2b427894727c · outbound

This paper cites Emergent abilities of large language models,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Emergent abilities of large language models,

Reference 25

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Observation 3d5d098c-19cd-4031-98bf-185fb044a788 · outbound

This paper cites On decoder-only architecture for speech- to-text and large language model integration,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition On decoder-only architecture for speech- to-text and large language model integration,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:56.251245Z

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Observation 68a369d3-bafa-4f07-92a9-a10bc310ae4c · outbound

This paper cites Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:56.315803Z

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Observation 6aa07d3c-ad30-467c-93df-e220f0b0dbb3 · outbound

This paper cites An Embarrassingly Simple Approach for LLM with Strong ASR Capacity.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition An Embarrassingly Simple Approach for LLM with Strong ASR Capacity

Reference 28

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Observation 018f2ebd-3399-4337-ab62-73768ed782ea · outbound

This paper cites Con- nectionist temporal classification: labelling unsegmented se- quence data with recurrent neural networks,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Con- nectionist temporal classification: labelling unsegmented se- quence data with recurrent neural networks,

Reference 29

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Observation 6ee8624f-cbc4-4fc5-9f1a-1ec1ff593aa4 · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive nlp tasks,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Retrieval- augmented generation for knowledge-intensive nlp tasks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:57.956952Z

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Observation c9987079-8909-430f-92d3-d4717c05477e · outbound

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

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Common voice: A massively-multilingual speech corpus,

Reference 31

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Observation 877caa9b-0464-4cac-8f78-601d21f01196 · outbound

This paper cites CAT: A CTC-CRF based ASR toolkit bridging the hybrid and the end-to-end approaches towards data efficiency and low latency,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition CAT: A CTC-CRF based ASR toolkit bridging the hybrid and the end-to-end approaches towards data efficiency and low latency,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:57.799600Z

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Observation e0974414-ba7e-4acc-9b51-fac04ce13836 · outbound

This paper cites Conformer: Convolution-augmented transformer for speech recognition,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Conformer: Convolution-augmented transformer for speech recognition,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:57.680435Z

Source-reported events for the cited work

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Observation 639b3a70-0520-4b8d-a7a7-7c68816bb3a5 · outbound

This paper cites Grapheme-to-phoneme transduction for cross-language ASR,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Grapheme-to-phoneme transduction for cross-language ASR,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:57.541032Z

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Observation e294896e-caa4-4936-9d41-c45f99ac750b · outbound

This paper cites Attention is all you need,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Attention is all you need,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:57.399654Z

Source-reported events for the cited work

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Observation 2edd59ce-ffb7-42ea-8bc8-02964cfa0135 · outbound

This paper cites Some statistical issues in the comparison of speech recognition algorithms,.

LLM-based phoneme-to-grapheme for phoneme-based speech recognition Some statistical issues in the comparison of speech recognition algorithms,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:40:57.254522Z

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.

Pith citing papers

Observation 44f88352-e306-4159-8cb3-6ec920acf1ef · inbound