Paper Citation Record · LEDGER

Pretraining Multi-Speaker Identification for Neural Speaker Diarization

As of 15 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2505.24545.

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

pith.paper-citation-record.v1
2505.24545 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:23:26.148077Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:23:24.209433Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:23:26.439493Z

Reference resolution

42 of 42 outbound references displayed

verified exact0
verified fuzzy30
unresolved9
parse uncertain0
malformed identifier2
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fa6f38c3-d3bf-45dc-9408-30a1217e80c4 · outbound

This paper cites an unresolved cited work.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:31.824700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7a67bcdb-cc66-49d2-bb45-d46264868983 · outbound

This paper cites Pretraining Multi-Speaker Identification for Neural Speaker Diarization.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Pretraining Multi-Speaker Identification for Neural Speaker Diarization

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T12:23:26.470279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 302b6b9b-cae0-4555-80a6-7fa951886a7e · outbound

This paper cites an unresolved cited work.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:23:31.648141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 04e71035-2c7e-4996-a8f3-55f81d408bf3 · outbound

This paper cites Dataset Table 1 lists the datasets used in our experiments, all monaural with a 16 kHz sampling rate and 16 bit depth.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Dataset Table 1 lists the datasets used in our experiments, all monaural with a 16 kHz sampling rate and 16 bit depth

Reference 4

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T12:23:31.551212Z

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Observation 7cc67e4a-0ad7-4932-898f-1fc177eef11b · outbound

This paper cites an unresolved cited work.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Unresolved cited work

Reference 5

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T12:23:26.376459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 56c5e90b-284c-4297-9c08-33dc0cd619f1 · outbound

This paper cites The method is storage-friendly, simulation-agnostic, and outperformed diarization-based pre- training, with further gains from additional DIA pretraining.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization The method is storage-friendly, simulation-agnostic, and outperformed diarization-based pre- training, with further gains from additional DIA pretraining

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:31.336895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b8809d20-5737-460d-a984-dbe51c289502 · outbound

This paper cites Front-end processing for the CHiME-5 dinner party scenario,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Front-end processing for the CHiME-5 dinner party scenario,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:31.232159Z

Source-reported events for the cited work

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Observation b0c7ff37-6e07-4931-815b-d549eeaa551a · outbound

This paper cites BUT/JHU system description for CHiME-8 NOTSOFAR-1 challenge,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization BUT/JHU system description for CHiME-8 NOTSOFAR-1 challenge,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:31.130816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4fd78155-4eda-4b50-bdae-3aa0155d3660 · outbound

This paper cites DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech Recognition.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech Recognition

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:23:24.503068Z

Source-reported events for the cited work

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Observation 8f0886cf-8faf-47d2-8ef1-b87c599b7fcc · outbound

This paper cites Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: Theory, implementation and analysis on standard tasks,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: Theory, implementation and analysis on standard tasks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:31.031489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8a0c71fa-f231-4cc8-b515-78c5257bf4b9 · outbound

This paper cites End-to-end neural speaker diarization with permutation-free objectives,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization End-to-end neural speaker diarization with permutation-free objectives,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:30.911215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b3a0e7b8-0949-4373-89f4-314dfc595c78 · outbound

This paper cites Integrating end-to- end neural and clustering-based diarization: Getting the best of both worlds,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Integrating end-to- end neural and clustering-based diarization: Getting the best of both worlds,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:30.787427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eb4bbdf1-6d12-4cf3-a6cc-2fff10a74ebe · outbound

This paper cites Towards neural diarization for unlimited num- bers of speakers using global and local attractors,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Towards neural diarization for unlimited num- bers of speakers using global and local attractors,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:30.621558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b398ad51-2fe1-4de3-8846-b22baa1f975f · outbound

This paper cites pyannote.audio 2.1 speaker diarization pipeline: prin- ciple, benchmark, and recipe,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization pyannote.audio 2.1 speaker diarization pipeline: prin- ciple, benchmark, and recipe,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:30.416238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ec649530-f800-4d20-b183-f6c97078bb05 · outbound

This paper cites Powerset multi-class cross entropy loss for neural speaker diarization,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Powerset multi-class cross entropy loss for neural speaker diarization,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:23:24.757297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96c7f9b9-a98b-413e-ab9f-fcfc510c4289 · outbound

This paper cites End-to-end diarization for variable number of speakers with local-global networks and discriminative speaker embeddings,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization End-to-end diarization for variable number of speakers with local-global networks and discriminative speaker embeddings,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:30.194665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation af22441e-1ddf-4bfd-ad56-afb18b638ad7 · outbound

This paper cites Improving the nat- uralness of simulated conversations for end-to-end neural diariza- tion,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Improving the nat- uralness of simulated conversations for end-to-end neural diariza- tion,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:29.935478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4640bd14-4b51-4c7e-9f4f-2348443458ab · outbound

This paper cites From simu- lated mixtures to simulated conversations as training data for end- to-end neural diarization,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization From simu- lated mixtures to simulated conversations as training data for end- to-end neural diarization,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:29.733367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4866b203-bbb6-4968-96b2-47775bc03f2c · outbound

This paper cites Leveraging self-supervised learning for speaker diarization,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Leveraging self-supervised learning for speaker diarization,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T12:23:24.948296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cdd0398-508a-47e6-8a3a-b80229a50690 · outbound

This paper cites Recursive attentive pooling for ex- tracting speaker embeddings from multi-speaker recordings,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Recursive attentive pooling for ex- tracting speaker embeddings from multi-speaker recordings,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:29.533149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dbd191cc-3ed4-4c50-bc9c-d252b9ec8d39 · outbound

This paper cites Frame-wise and overlap-robust speaker em- beddings for meeting diarization,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Frame-wise and overlap-robust speaker em- beddings for meeting diarization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:29.327882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 56bfb1df-580a-45e6-9424-d00bd60606d0 · outbound

This paper cites Leverag- ing speaker embeddings in end-to-end neural diarization for two- speaker scenarios,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Leverag- ing speaker embeddings in end-to-end neural diarization for two- speaker scenarios,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:29.093717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6ca7aa5a-6933-4a38-8733-c216fca7ca2c · outbound

This paper cites ECAPA- TDNN: Emphasized channel attention, propagation and aggrega- tion in TDNN based speaker verification,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization ECAPA- TDNN: Emphasized channel attention, propagation and aggrega- tion in TDNN based speaker verification,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:28.859536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1f3a1947-1320-4bcd-949c-0c8d2221052f · outbound

This paper cites Reshape dimensions network for speaker recognition,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Reshape dimensions network for speaker recognition,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:28.722290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation de2e1446-1b7f-493e-a055-3a7700970a66 · outbound

This paper cites Advances in inte- gration of end-to-end neural and clustering-based diarization for real conversational speech,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Advances in inte- gration of end-to-end neural and clustering-based diarization for real conversational speech,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:28.588649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cd6c1782-ba22-4039-8b68-711ca17f8fd7 · outbound

This paper cites BUT system for the Second DIHARD Speech Diarization Challenge,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization BUT system for the Second DIHARD Speech Diarization Challenge,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:28.417571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4899405e-6860-4eee-9c96-7cdbbd5ed1ce · outbound

This paper cites Overlap-aware diarization: Resegmentation using neural end-to-end overlapped speech detection,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Overlap-aware diarization: Resegmentation using neural end-to-end overlapped speech detection,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:28.252601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 726fe55b-17bc-4a87-8b89-1090e5347b9d · outbound

This paper cites End-to-end speaker diarization as post-processing,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization End-to-end speaker diarization as post-processing,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:28.090233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 91cc6fbe-5938-4df6-ac68-07e0df910299 · outbound

This paper cites V oxCeleb: Large-scale speaker verification in the wild,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization V oxCeleb: Large-scale speaker verification in the wild,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T12:23:25.476790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34ab085d-3ddb-4273-a435-464d5cd83218 · outbound

This paper cites AISHELL-4: An open source dataset for speech enhancement, separation, recognition and speaker diarization in conference scenario,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization AISHELL-4: An open source dataset for speech enhancement, separation, recognition and speaker diarization in conference scenario,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:27.876180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 04b31024-6d93-4ea6-8fae-0f9dbfd7d4dd · outbound

This paper cites M2MeT: The ICASSP 2022 multi-channel multi-party meeting transcription challenge,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization M2MeT: The ICASSP 2022 multi-channel multi-party meeting transcription challenge,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:27.725928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cbc61f7e-cf58-4393-ad8c-947602ca255f · outbound

This paper cites Unleashing the killer corpus: experiences in creating the multi-everything AMI Meeting Corpus,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Unleashing the killer corpus: experiences in creating the multi-everything AMI Meeting Corpus,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T12:23:25.654858Z

Source-reported events for the cited work

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Observation f58573fb-f466-444b-9592-5939b85072ec · outbound

This paper cites Open source MagicData-RAMC: A rich annotated Mandarin conversational (RAMC) speech dataset,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Open source MagicData-RAMC: A rich annotated Mandarin conversational (RAMC) speech dataset,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:27.586119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4ec45dcc-566a-4e00-bb24-dc6159583a7f · outbound

This paper cites MSDWild: Multi- modal speaker diarization dataset in the wild,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization MSDWild: Multi- modal speaker diarization dataset in the wild,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:27.487926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2776752f-c287-48e1-9e9b-3223340ddcdd · outbound

This paper cites Spot the conversation: Speaker diarisation in the wild,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Spot the conversation: Speaker diarisation in the wild,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:27.368891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ab377e1a-3d41-4596-b408-a8823e459ddb · outbound

This paper cites Mamba-based segmentation model for speaker diariza- tion,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Mamba-based segmentation model for speaker diariza- tion,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:27.268159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5bb5f6e7-1629-4b54-8eec-8e3d1fc084f6 · outbound

This paper cites FunASR: A fundamental end-to- end speech recognition toolkit,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization FunASR: A fundamental end-to- end speech recognition toolkit,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:27.127955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e6d6392e-8038-4318-b0e3-6156ff8dcab9 · outbound

This paper cites Adam: A method for stochastic opti- mization,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Adam: A method for stochastic opti- mization,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T12:23:25.928738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 596f3de0-db7e-4c60-91eb-bfffefa9b36d · outbound

This paper cites Speaker recognition from raw wave- form with SincNet,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization Speaker recognition from raw wave- form with SincNet,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:26.902853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 45a5248f-e6c9-4385-a67a-f68767665b13 · outbound

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

Pretraining Multi-Speaker Identification for Neural Speaker Diarization WavLM: Large-scale self- supervised pre-training for full stack speech processing,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T12:23:26.036715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2d5bea5-8bfe-4d6a-b3db-4cb21d3cab49 · outbound

This paper cites pyan- note.audio speaker diarization pipeline at V oxSRC 2023,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization pyan- note.audio speaker diarization pipeline at V oxSRC 2023,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:26.780187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a57dcd30-dee0-4630-aebf-44ed39609dfa · outbound

This paper cites NTT speaker diarization system for CHiME-7: Multi-domain, multi- microphone end-to-end and vector clustering diarization,.

Pretraining Multi-Speaker Identification for Neural Speaker Diarization NTT speaker diarization system for CHiME-7: Multi-domain, multi- microphone end-to-end and vector clustering diarization,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:26.629779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 7a67bcdb-cc66-49d2-bb45-d46264868983 · inbound