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Paper Citation Record · LEDGER

Error Analysis in a Modular Meeting Transcription System

As of 19 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2509.10143.

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

pith.paper-citation-record.v1
2509.10143 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:10:40.677516Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

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Source: cited_works

Reference resolution

28 of 28 outbound references displayed

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Outbound references

Observation 81b91f03-1a3e-4c87-8574-31680bcf24c7 · outbound

This paper cites Continuous speech separation with conformer,.

Error Analysis in a Modular Meeting Transcription System Continuous speech separation with conformer,

Reference 1

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source=pdf_text observed=2026-08-04T18:10:38.514199Z digest=sha256:35bb87627404553b425f2274b223d7ca17130995ec6a49889844bc0afc9e8ee8

Observation 9b489ac8-b125-46dd-9c6e-990251338edd · outbound

This paper cites Mixture encoder for joint speech separation and recognition,.

Error Analysis in a Modular Meeting Transcription System Mixture encoder for joint speech separation and recognition,

Reference 2

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source=pdf_text observed=2026-08-04T18:10:38.589962Z digest=sha256:00e06e714604eee6a1c7a797ab5032637d4df8623533109f3d7d9eab08010182

Observation 3417e6ae-aeb9-4368-96f8-5f62ec8909ec · outbound

This paper cites Multi-turn rnn-t for streaming recognition of multi-party speech,.

Error Analysis in a Modular Meeting Transcription System Multi-turn rnn-t for streaming recognition of multi-party speech,

Reference 3

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source=pdf_text observed=2026-08-04T18:10:38.676537Z digest=sha256:a50ca77a3dc45602cd6a6d751bd254859b4d60793654efb5ad28461ae6533d8e

Observation 30385cdf-1b12-43ca-a2a8-8ed326da73d0 · outbound

This paper cites Streaming Multi-Talker ASR with Token-Level Serialized Output Training.

Error Analysis in a Modular Meeting Transcription System Streaming Multi-Talker ASR with Token-Level Serialized Output Training

Reference 4

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source=pdf_text observed=2026-08-04T18:10:38.759180Z digest=sha256:053d00ae9dd87d9f5bffcbcad1ee092628604d607957e3477973cdf680d09b8f

Observation 9b6b078e-5f70-4be2-a262-4685a8dd3f81 · outbound

This paper cites Continuous speech separation: Dataset and analysis,.

Error Analysis in a Modular Meeting Transcription System Continuous speech separation: Dataset and analysis,

Reference 5

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source=pdf_text observed=2026-08-04T18:10:38.870161Z digest=sha256:61b70393ea34cf66adfe8800da04c776064e183b4be2a98d2f2afb91fefff0ec

Observation 92ce3ad9-29f1-4c8e-8fe5-3ac53f482f5b · outbound

This paper cites TF-GridNet: Integrating full- and sub-band modeling for speech separation,.

Error Analysis in a Modular Meeting Transcription System TF-GridNet: Integrating full- and sub-band modeling for speech separation,

Reference 6

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source=pdf_text observed=2026-08-04T18:10:38.954881Z digest=sha256:476c7bf8bcf23173063b0f6c1948757f7ef7cc72c76d51ce8e15b34163197cce

Observation f30a8c1b-5b4c-4cf0-8125-89fd3c7548f7 · outbound

This paper cites TS-SEP: Joint diarization and separation conditioned on estimated speaker embeddings,.

Error Analysis in a Modular Meeting Transcription System TS-SEP: Joint diarization and separation conditioned on estimated speaker embeddings,

Reference 7

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source=pdf_text observed=2026-08-04T18:10:39.012615Z digest=sha256:6b20025436ee6f0ab6d8d9c474f7a4dbb8a819ba783b707b08ee8dfa3224041d

Observation f9378cd3-c9d8-486d-9d76-0801c900c6a0 · outbound

This paper cites M2Met: The ICASSP 2022 multi-channel multi-party meeting transcrip- tion challenge,.

Error Analysis in a Modular Meeting Transcription System M2Met: The ICASSP 2022 multi-channel multi-party meeting transcrip- tion challenge,

Reference 8

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source=pdf_text observed=2026-08-04T18:10:39.098729Z digest=sha256:37e1072fb5fbcb5e9fad7a14883b316c8086c5817e15d99044ae4660206a2790

Observation 00bdcc90-6aec-4a9e-b042-5533795c3725 · outbound

This paper cites How bad are artifacts?: Analyzing the impact of speech enhancement errors on ASR,.

Error Analysis in a Modular Meeting Transcription System How bad are artifacts?: Analyzing the impact of speech enhancement errors on ASR,

Reference 9

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source=pdf_text observed=2026-08-04T18:10:39.153160Z digest=sha256:86411d4581651d87e3b3bb8ccf38a1fd01d040e0a1df0b57a2ae09b9cc736286

Observation 549b5d3c-3cf4-4a43-9f58-ea7130bb9f7d · outbound

This paper cites Impact of residual noise and artifacts in speech enhancement errors on intelligibility of human and machine,.

Error Analysis in a Modular Meeting Transcription System Impact of residual noise and artifacts in speech enhancement errors on intelligibility of human and machine,

Reference 10

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Observation 375abbde-9c4b-4169-a737-edf68eae8e97 · outbound

This paper cites Monaural source separation: From anechoic to reverberant environments,.

Error Analysis in a Modular Meeting Transcription System Monaural source separation: From anechoic to reverberant environments,

Reference 11

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source=pdf_text observed=2026-08-04T18:10:39.317549Z digest=sha256:bf57ee80c57a48811fb6ab308f704c7eb1466eb5b7acb26ca06b6ee32da7a46c

Observation 35a01af6-0c7d-487a-964e-a9db6e622226 · outbound

This paper cites Explicit word error minimization using word hypothesis posterior probabilities,.

Error Analysis in a Modular Meeting Transcription System Explicit word error minimization using word hypothesis posterior probabilities,

Reference 12

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source=pdf_text observed=2026-08-04T18:10:39.400331Z digest=sha256:32a1097e7cf59b8600ef621ce9c53c9f71edcdd05d8577aa583835b1c43e79ae

Observation 4ddabb42-2a41-4b23-a410-351fb849b1b6 · outbound

This paper cites Con- fidence measures for large vocabulary continuous speech recognition,.

Error Analysis in a Modular Meeting Transcription System Con- fidence measures for large vocabulary continuous speech recognition,

Reference 13

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source=pdf_text observed=2026-08-04T18:10:39.454327Z digest=sha256:caa904058c6f5c4ece6ac295edc29110be7ed6277a3e6dc6699be5e8a26c9eb2

Observation bc96ee97-ebd7-4a75-87e3-639bb18e04a1 · outbound

This paper cites Combining TF-GridNet and mixture encoder for continuous speech separation for meeting transcription,.

Error Analysis in a Modular Meeting Transcription System Combining TF-GridNet and mixture encoder for continuous speech separation for meeting transcription,

Reference 14

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source=pdf_text observed=2026-08-04T18:10:39.538992Z digest=sha256:fb5525820cf908f7ea0d4a01a1fb0167fceccc2556249abb7ebb9ac04cda363f

Observation 66b8bade-1646-4fe3-ab59-a7af7b9f1eaf · outbound

This paper cites Recognizing overlapped speech in meetings: A multichan- nel separation approach using neural networks,.

Error Analysis in a Modular Meeting Transcription System Recognizing overlapped speech in meetings: A multichan- nel separation approach using neural networks,

Reference 15

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source=pdf_text observed=2026-08-04T18:10:39.625107Z digest=sha256:6afd55d5776c9c4d0ef6bafa022361652263aaa5038054083b640e2f5f0d6158

Observation 5345d1f5-699e-43b7-97b6-d8a5dcf4fc56 · outbound

This paper cites TF-GridNet: Making time-frequency domain models great again for monaural speaker separation,.

Error Analysis in a Modular Meeting Transcription System TF-GridNet: Making time-frequency domain models great again for monaural speaker separation,

Reference 16

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source=pdf_text observed=2026-08-04T18:10:39.707336Z digest=sha256:b1a58b2017b75cb91972a4695f3648c48a310edc32970a8e1ad6f371fc2a455d

Observation c501d8c1-80e8-476d-bcb6-3fd6d3c6963c · outbound

This paper cites Meeting recognition with con- tinuous speech separation and transcription-supported di- arization,.

Error Analysis in a Modular Meeting Transcription System Meeting recognition with con- tinuous speech separation and transcription-supported di- arization,

Reference 17

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source=pdf_text observed=2026-08-04T18:10:39.791509Z digest=sha256:a14c96ba8dd16221eb2063470c1d6c7042c19ed1a69609ba687dde2fda064524

Observation 92e5f0f6-1c1f-4abb-88ba-5bb55a9cc080 · outbound

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

Error Analysis in a Modular Meeting Transcription System Robust speech recognition via large-scale weak supervision,

Reference 18

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source=pdf_text observed=2026-08-04T18:10:39.845750Z digest=sha256:651f7fa6996a1417eea8d06f4f9f9bb8129a9400e6d90583e47e5a7e4c0db716

Observation bd73d1f9-474a-4cd6-999a-71d8bcc6ca43 · outbound

This paper cites Integration of speech separa- tion, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis,.

Error Analysis in a Modular Meeting Transcription System Integration of speech separa- tion, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis,

Reference 19

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Observation ae8a23a4-3af4-4cd4-b4a0-da7a999f7e53 · outbound

This paper cites Once more diarization: Improving meeting transcription systems through segment-level speaker reassignment,.

Error Analysis in a Modular Meeting Transcription System Once more diarization: Improving meeting transcription systems through segment-level speaker reassignment,

Reference 20

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Observation 76be257e-ad90-4e03-b668-4f3fda46443f · outbound

This paper cites DCF-DS: Deep Cascade Fusion of Diarization and Separation for Speech Recognition under Realistic Single-Channel Conditions.

Error Analysis in a Modular Meeting Transcription System DCF-DS: Deep Cascade Fusion of Diarization and Separation for Speech Recognition under Realistic Single-Channel Conditions

Reference 21

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source=pdf_text observed=2026-08-04T18:10:40.078010Z digest=sha256:5b27c0488376776199954fb4cfc76a49bdbb09f91c1d54891b61ad187678ad7a

Observation 87c21bf6-8b17-43d6-85ab-2e5308e7384b · outbound

This paper cites MeetEval: A toolkit for computation of word error rates for meeting transcription systems,.

Error Analysis in a Modular Meeting Transcription System MeetEval: A toolkit for computation of word error rates for meeting transcription systems,

Reference 22

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source=pdf_text observed=2026-08-04T18:10:40.157348Z digest=sha256:a5b057cf77afbba135c920110b03f250527dbe4e7e6a7d39ab6ae095d693daa3

Observation 1fbd8dd8-80a2-4797-b78d-f4f1d0d9bd3a · outbound

This paper cites Lib- riSpeech: An ASR corpus based on public domain audio books,.

Error Analysis in a Modular Meeting Transcription System Lib- riSpeech: An ASR corpus based on public domain audio books,

Reference 23

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source=pdf_text observed=2026-08-04T18:10:40.238807Z digest=sha256:49700f60a4708f0e61c65ee37760831d4e34b76cd71e863aa41c48c18b926e39

Observation c699054d-36e7-4dd4-aede-b0776dd0b348 · outbound

This paper cites SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition.

Error Analysis in a Modular Meeting Transcription System SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition

Reference 24

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Observation d920977f-7702-41cc-965b-5858e6b50eba · outbound

This paper cites Language mod- eling with deep transformers,.

Error Analysis in a Modular Meeting Transcription System Language mod- eling with deep transformers,

Reference 25

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Observation ae4beec9-4e1f-4528-b7e4-06fd86d080b6 · outbound

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

Error Analysis in a Modular Meeting Transcription System Con- former: Convolution-augmented transformer for speech recognition,

Reference 26

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source=pdf_text observed=2026-08-04T18:10:40.488271Z digest=sha256:04cb7825080a65a0ce98e75c515074c5a429a4e478ff5be7118d9fe5a2a5b225

Observation 45e2cc10-4608-4fc7-91c5-9fb1c07307f8 · outbound

This paper cites WavLM: Large-scale self-supervised pre-training for full stack speech process- ing,.

Error Analysis in a Modular Meeting Transcription System WavLM: Large-scale self-supervised pre-training for full stack speech process- ing,

Reference 27

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Observation 61ade70f-a860-49b4-aef1-71c4f9740ec5 · outbound

This paper cites Performance measurement in blind audio source separation,.

Error Analysis in a Modular Meeting Transcription System Performance measurement in blind audio source separation,

Reference 28

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Pith citing papers

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