Paper Citation Record · LEDGER

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition

As of 21 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2509.04392.

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

pith.paper-citation-record.v1
2509.04392 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:16:04.899137Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

42 of 42 outbound references displayed

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verified fuzzy31
unresolved11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0309f99b-a889-4a31-b686-e47c116611c9 · outbound

This paper cites Automatic channel selection and spatial feature integration for multi-channel speech recog- nition across various array topologies,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Automatic channel selection and spatial feature integration for multi-channel speech recog- nition across various array topologies,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.354529Z

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Observation a2bd4b7a-dd85-49fc-a124-480539383b13 · outbound

This paper cites Deliberation model based two-pass end-to-end speech recognition,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Deliberation model based two-pass end-to-end speech recognition,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.343299Z

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Observation e9f6646a-f612-45e7-9ab4-9e8cb3262710 · outbound

This paper cites An analysis of incorporating an external language model into a sequence-to-sequence model,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition An analysis of incorporating an external language model into a sequence-to-sequence model,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.332351Z

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Observation ec706002-a978-4feb-a47e-7adcda5b034c · outbound

This paper cites Component fusion: Learning replaceable language model component for end-to-end speech recognition system,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Component fusion: Learning replaceable language model component for end-to-end speech recognition system,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.321181Z

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Observation 04772e2b-b4d5-498c-9982-a514fa7831fd · outbound

This paper cites Fastcorrect: Fast error correction with edit alignment for automatic speech recognition,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Fastcorrect: Fast error correction with edit alignment for automatic speech recognition,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.310209Z

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Observation bc9cd86b-4f77-4eca-a34a-1b2f558f1451 · outbound

This paper cites Fastcorrect: Fast error correction with edit alignment for automatic speech recognition,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Fastcorrect: Fast error correction with edit alignment for automatic speech recognition,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.299551Z

Source-reported events for the cited work

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Observation 9f5354d9-3315-4a59-9f61-2464881b37a1 · outbound

This paper cites Improving readability for automatic speech recognition tran- scription,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Improving readability for automatic speech recognition tran- scription,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.288101Z

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Observation c6834d8d-4715-4040-a009-e627e31e2e2c · outbound

This paper cites N-best t5: Robust asr error correction using multiple input hypotheses and constrained decoding space,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition N-best t5: Robust asr error correction using multiple input hypotheses and constrained decoding space,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.277826Z

Source-reported events for the cited work

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Observation 7214771b-f77a-49a8-92e0-fbef963d77d0 · outbound

This paper cites Towards interfacing large language models with asr systems using confidence measures and prompting,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Towards interfacing large language models with asr systems using confidence measures and prompting,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.267846Z

Source-reported events for the cited work

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Observation f98c4fd8-8af0-4a10-8d27-75b13f2417a8 · outbound

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

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Hyporadise: An open baseline for generative speech recognition with large language models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.257731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f730f8bd-92e0-4c88-87e6-a26e89ba61cb · outbound

This paper cites Noise-robust speech recognition with 10 minutes unparalleled in-domain data,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Noise-robust speech recognition with 10 minutes unparalleled in-domain data,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.247060Z

Source-reported events for the cited work

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Observation c7b9d0cc-8ff4-49ae-aab5-16c9101fd2e5 · outbound

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

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition It’s never too late: Fusing acoustic information into large language models for automatic speech recognition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.236031Z

Source-reported events for the cited work

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Observation 5131510e-5801-4fbd-be2f-c264987c1871 · outbound

This paper cites Whispering llama: A cross-modal generative error correction framework for speech recognition,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Whispering llama: A cross-modal generative error correction framework for speech recognition,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.224749Z

Source-reported events for the cited work

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Observation edd05fb2-d02b-4d5f-94aa-261a4b3cba1e · outbound

This paper cites Connecting speech encoder and large language model for asr,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Connecting speech encoder and large language model for asr,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.214180Z

Source-reported events for the cited work

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Observation 73c026fc-d225-4822-8421-2d29ca8bfe0a · outbound

This paper cites Listen again and choose the right answer: A new paradigm for automatic speech recognition with large language models,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Listen again and choose the right answer: A new paradigm for automatic speech recognition with large language models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.203527Z

Source-reported events for the cited work

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Observation 006e356c-0992-49bd-b8cf-1b5c5f59cc2d · outbound

This paper cites Mmger: Multi-modal and multi-granularity generative error correction with llm for joint accent and speech recognition,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Mmger: Multi-modal and multi-granularity generative error correction with llm for joint accent and speech recognition,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.192010Z

Source-reported events for the cited work

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Observation 791ec010-1c49-4085-8a11-a51d2edb1e65 · outbound

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

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

Reference 17

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Source-reported events for the cited work

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Observation 16abd9eb-df37-4f74-9542-77af2a53c1b2 · outbound

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

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition An Embarrassingly Simple Approach for LLM with Strong ASR Capacity

Reference 18

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Observation fb62c280-17c3-4e2c-b016-b855de249411 · outbound

This paper cites Salmonn: Towards generic hearing abilities for large lan- guage models,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Salmonn: Towards generic hearing abilities for large lan- guage models,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.182059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 04956550-ec28-4de8-b332-b4fc7b616d28 · outbound

This paper cites Prompting large language models with speech recognition abilities,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Prompting large language models with speech recognition abilities,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.171300Z

Source-reported events for the cited work

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Observation 659efc10-769a-4d24-989d-bad7907b011a · outbound

This paper cites Slm: Bridge the thin gap between speech and text foundation models,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Slm: Bridge the thin gap between speech and text foundation models,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.161022Z

Source-reported events for the cited work

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Observation 4880aade-3fb7-4e76-bbcc-b014742743a2 · outbound

This paper cites Chatting about chatgpt: how may ai and gpt impact academia and libraries?,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Chatting about chatgpt: how may ai and gpt impact academia and libraries?,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.151281Z

Source-reported events for the cited work

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Observation ae06776a-8285-42a1-9cf4-cb8b69637ee0 · outbound

This paper cites GPT-4 Technical Report.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition GPT-4 Technical Report

Reference 23

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Observation 2a9d6266-bbf9-4a4f-853b-c58ec648a8d5 · outbound

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

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition LLaMA: Open and Efficient Foundation Language Models

Reference 24

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Observation 7ce5cd9b-fce4-4ece-82fa-21e0ffbfe57f · outbound

This paper cites Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.140908Z

Source-reported events for the cited work

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Observation 41bc5a63-d4dc-4a4d-8689-e30e4d35f1c7 · outbound

This paper cites Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration

Reference 26

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Source-reported events for the cited work

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Observation 5c30e5ff-2c78-42b8-a721-b8a8543463d7 · outbound

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

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition On decoder-only architecture for speech-to-text and large language model integration,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.130322Z

Source-reported events for the cited work

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Observation 5717e209-f013-4b6d-90b2-82114cc72974 · outbound

This paper cites Seamless: Multilingual Expressive and Streaming Speech Translation.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Seamless: Multilingual Expressive and Streaming Speech Translation

Reference 28

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Source-reported events for the cited work

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Observation 03e345c8-3c81-484f-b566-23eca600407b · outbound

This paper cites AudioPaLM: A Large Language Model That Can Speak and Listen.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition AudioPaLM: A Large Language Model That Can Speak and Listen

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T10:16:04.853044Z

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Observation c73c2754-a00d-4cfb-9a52-9371bea6725a · outbound

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

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Can Generative Large Language Models Perform ASR Error Correction?

Reference 30

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Observation bdfe3de1-8c6a-41e3-9c31-6a1614c2c788 · outbound

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

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Robust speech recognition via large-scale weak supervi- sion,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.119669Z

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Observation 8087cc9f-4575-4906-b0a2-e6895501d1bb · outbound

This paper cites Beats: Audio pre-training with acoustic tokenizers,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Beats: Audio pre-training with acoustic tokenizers,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.108573Z

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Observation c9a338d3-7211-4f12-8110-baff576a0ab8 · outbound

This paper cites Speechgpt: Empowering large language models with intrinsic cross- modal conversational abilities,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Speechgpt: Empowering large language models with intrinsic cross- modal conversational abilities,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.097718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 07d5ce17-2a58-4b1e-a4b3-ef22bb2a2ef7 · outbound

This paper cites Librispeech: an asr corpus based on public domain audio books,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Librispeech: an asr corpus based on public domain audio books,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.087400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b3a2b0ee-d733-440c-89cf-0f054f455eb8 · outbound

This paper cites Mrcn: A novel modality restitution and compensation network for visible-infrared person re-identification,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Mrcn: A novel modality restitution and compensation network for visible-infrared person re-identification,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.075328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9d3de821-eb26-4252-8059-22b78a4dbc41 · outbound

This paper cites Multi-stage auxiliary learning for visible-infrared person re-identification,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Multi-stage auxiliary learning for visible-infrared person re-identification,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.064186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 09b12923-4b21-4f31-a03c-f2275b0f650f · outbound

This paper cites Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T10:16:04.880310Z

Source-reported events for the cited work

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Observation 6b3429fe-9157-4481-89c1-34de5ae81691 · outbound

This paper cites MUSAN: A Music, Speech, and Noise Corpus.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition MUSAN: A Music, Speech, and Noise Corpus

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T10:16:04.884569Z

Source-reported events for the cited work

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Observation 07ca5e88-5e26-4971-886c-9fde1fac9ed6 · outbound

This paper cites The design for the wall street journal-based csr corpus,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition The design for the wall street journal-based csr corpus,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.053004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e223f097-a1fe-4fa6-acd7-5e5596a6bd51 · outbound

This paper cites The rwth/upb/forth system combination for the 4th chime challenge evaluation,.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition The rwth/upb/forth system combination for the 4th chime challenge evaluation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.042194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0fe4e747-0118-49f9-932a-aadda61ae897 · outbound

This paper cites Qwen Technical Report.

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Qwen Technical Report

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T10:16:04.895681Z

Source-reported events for the cited work

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Observation bc0bd940-5565-4035-be56-64ba40a39127 · outbound

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

Denoising GER: A Noise-Robust Generative Error Correction with LLM for Speech Recognition Lora: Low-rank adaptation of large language models,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:16:05.029934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.