Paper Citation Record · LEDGER

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge

As of 17 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2505.16387.

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

pith.paper-citation-record.v1
2505.16387 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:04:51.033073Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:04:50.835877Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:04:51.192111Z

Reference resolution

43 of 43 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ff59d05b-1346-4ce4-9ceb-0f537d686407 · outbound

This paper cites Who-Spoke-When.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Who-Spoke-When

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.667337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0de7e9ae-52dc-4b9d-8d73-48d9d8f1c991 · outbound

This paper cites Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:04:51.197606Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6acf41ae-a7d0-42d4-83d8-baab8a4aea9c · outbound

This paper cites Datasets For the simulated data, we combine the V oxCeleb2 [26], V oxBlink2 [27], KeSpeech [28], and 3D-Speaker [29] datasets to create a large-scale corpus with 153,738 identities.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Datasets For the simulated data, we combine the V oxCeleb2 [26], V oxBlink2 [27], KeSpeech [28], and 3D-Speaker [29] datasets to create a large-scale corpus with 153,738 identities

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.651341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 328c2e8e-1999-43f2-a8a3-e178c17765b8 · outbound

This paper cites Table 1 illustrates the performance of the systems we developed on the MISP-Meeting evaluation set.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Table 1 illustrates the performance of the systems we developed on the MISP-Meeting evaluation set

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.635700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f6d028d2-818d-4f78-96b6-55ad7a7d12de · outbound

This paper cites Com- pared to the original S2SND method, the modified MC-S2SND model effectively processes multi-channel audio to enhance di- arization performance in offline scenarios.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Com- pared to the original S2SND method, the modified MC-S2SND model effectively processes multi-channel audio to enhance di- arization performance in offline scenarios

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.619532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 14d97572-731d-43b3-af50-295161de1123 · outbound

This paper cites Many thanks for the computational resource provided by the Advanced Computing East China Sub-Center.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Many thanks for the computational resource provided by the Advanced Computing East China Sub-Center

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.604807Z

Source-reported events for the cited work

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Observation af42a6cf-17c1-4954-bf4b-b0bd409c2c85 · outbound

This paper cites A review of speaker diarization: Recent advances with deep learning,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge A review of speaker diarization: Recent advances with deep learning,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:50.861821Z

Source-reported events for the cited work

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Observation cfd3b740-ff6a-4d5f-9570-7f2540c48129 · outbound

This paper cites Joint speaker counting, speech recognition, and speaker identification for overlapped speech of any number of speakers,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Joint speaker counting, speech recognition, and speaker identification for overlapped speech of any number of speakers,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.581294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bbc4fdcd-54b5-4ab9-ae62-01e247e3237f · outbound

This paper cites Speaker diarization with lstm,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Speaker diarization with lstm,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.566799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8450cace-506b-4478-bcc6-a9286837a12f · outbound

This paper cites Lstm based sim- ilarity measurement with spectral clustering for speaker diariza- tion,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Lstm based sim- ilarity measurement with spectral clustering for speaker diariza- tion,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.550686Z

Source-reported events for the cited work

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Observation a36f8174-d753-44fc-8f59-ccda5ccf6b28 · outbound

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

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Bayesian hmm clustering of x-vector sequences (vbx) in speaker diarization: Theory, implementation and analysis on standard tasks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.536558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ef7f3059-9101-4307-b8c3-6032145a0e56 · outbound

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

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge End-to-end neural speaker diarization with permutation-free objectives,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.522384Z

Source-reported events for the cited work

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Observation b5a4fff9-5f47-48ca-af1d-703412cbe963 · outbound

This paper cites End-to-end speaker diarization for an unknown number of speakers with encoder-decoder based attractors,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge End-to-end speaker diarization for an unknown number of speakers with encoder-decoder based attractors,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.507286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7ef2a076-459d-430d-b7f6-7de2abd23028 · outbound

This paper cites Encoder-decoder based attractors for end-to-end neural diariza- tion,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Encoder-decoder based attractors for end-to-end neural diariza- tion,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.493107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f0916887-cebe-4cec-9c53-abd8b511be68 · outbound

This paper cites Deep clus- tering: Discriminative embeddings for segmentation and separa- tion,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Deep clus- tering: Discriminative embeddings for segmentation and separa- tion,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:50.900893Z

Source-reported events for the cited work

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Observation c43f01bd-6bad-4b03-b652-b706f6296b8f · outbound

This paper cites Target- speaker voice activity detection: A novel approach for multi- speaker diarization in a dinner party scenario,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Target- speaker voice activity detection: A novel approach for multi- speaker diarization in a dinner party scenario,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.470753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 48153864-c342-4633-86fa-fc8b039e549a · outbound

This paper cites Similarity measure- ment of segment-level speaker embeddings in speaker diariza- tion,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Similarity measure- ment of segment-level speaker embeddings in speaker diariza- tion,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.455913Z

Source-reported events for the cited work

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Observation 0ba162f9-a227-4fc7-b8db-9119f6147e33 · outbound

This paper cites Target- speaker voice activity detection via sequence-to-sequence predic- tion,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Target- speaker voice activity detection via sequence-to-sequence predic- tion,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.440280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 33c8981e-dab8-4705-bd32-0cc930f0b157 · outbound

This paper cites USTC-NELSLIP System Description for DIHARD-III Challenge.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge USTC-NELSLIP System Description for DIHARD-III Challenge

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:50.918561Z

Source-reported events for the cited work

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Observation 9086adb4-75f2-42c7-9ddf-3c3ae24c64ad · outbound

This paper cites The DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge The DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:50.923535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a201cff-f520-436e-b98d-7795a6ddfd09 · outbound

This paper cites The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:04:51.146845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 099cacf2-274c-4ba5-9bb5-3dddd2a354f1 · outbound

This paper cites The dku-msxf diarization system for the voxceleb speaker recog- nition challenge 2023,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge The dku-msxf diarization system for the voxceleb speaker recog- nition challenge 2023,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.423624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 30ea4a2f-dcd0-413f-9165-dfbfa4e3d95b · outbound

This paper cites Sequence-to-Sequence Neural Diarization with Automatic Speaker Detection and Representation.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Sequence-to-Sequence Neural Diarization with Automatic Speaker Detection and Representation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:50.938259Z

Source-reported events for the cited work

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Observation ae8fd973-823e-42b6-9d5b-e5266ae0f6cd · outbound

This paper cites Diarization is hard: Some experiences and lessons learned for the jhu team in the inaugural dihard challenge,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Diarization is hard: Some experiences and lessons learned for the jhu team in the inaugural dihard challenge,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.408907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6e736b10-44d7-4834-9ff7-49a35d3d2260 · outbound

This paper cites The second dihard diarization challenge: Dataset, task, and baselines,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge The second dihard diarization challenge: Dataset, task, and baselines,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.394143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b8d2681a-be74-45e1-92a6-dc75a8f556e2 · outbound

This paper cites The third di- hard diarization challenge,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge The third di- hard diarization challenge,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.379762Z

Source-reported events for the cited work

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Observation bb2f67d5-2367-4883-a667-f104d99c63c6 · outbound

This paper cites The multimodal information based speech processing (misp) 2025 challenge: Audio-visual di- arization and recognition,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge The multimodal information based speech processing (misp) 2025 challenge: Audio-visual di- arization and recognition,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.364661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 998ca41b-7d33-42ef-bf9e-9ba2f545417a · outbound

This paper cites Deep residual learning for image recognition,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Deep residual learning for image recognition,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:50.963445Z

Source-reported events for the cited work

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Observation a7c2b1e5-4353-4691-a5f8-26b13e84b30e · outbound

This paper cites Attention is all you need,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Attention is all you need,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.341947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2c35ab47-50fd-4b7e-82ef-e86a4a32910d · outbound

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

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Conformer: Convolution-augmented transformer for speech recognition,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.327881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 959f78fb-ba96-4ee3-a990-f8241cded5a9 · outbound

This paper cites Arcface: Additive angular margin loss for deep face recognition,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Arcface: Additive angular margin loss for deep face recognition,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.313556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 57b521cd-30c9-46ff-a057-28c64dda53a0 · outbound

This paper cites V oxceleb2: Deep speaker recognition,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge V oxceleb2: Deep speaker recognition,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:50.981049Z

Source-reported events for the cited work

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Observation 277052db-059a-4e16-9b45-b737e5fe281b · outbound

This paper cites V oxblink2: A 100k+ speaker recognition corpus and the open- set speaker-identification benchmark,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge V oxblink2: A 100k+ speaker recognition corpus and the open- set speaker-identification benchmark,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.289822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f46567b2-4a5c-4f56-a70b-7d7b0ab1001d · outbound

This paper cites Kespeech: An open source speech dataset of mandarin and its eight subdialects,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Kespeech: An open source speech dataset of mandarin and its eight subdialects,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:50.991450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2722b4e-f906-42c7-a0ce-6e054e013cac · outbound

This paper cites 3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge 3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:50.995609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c45c1e7-7a83-4781-afae-c1fc57acfaf0 · outbound

This paper cites Multi-Input Multi-Output Target-Speaker Voice Activity Detection For Unified, Flexible, and Robust Audio-Visual Speaker Diarization.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Multi-Input Multi-Output Target-Speaker Voice Activity Detection For Unified, Flexible, and Robust Audio-Visual Speaker Diarization

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:51.000297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6ab55b0-241d-4a63-b041-6a32f41a13df · outbound

This paper cites MISP-Meeting: A real-world dataset with multimodal cues for long-form meeting transcription and summarization,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge MISP-Meeting: A real-world dataset with multimodal cues for long-form meeting transcription and summarization,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.267025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 32e4cac3-d693-4776-8be2-4ffce9b70f1f · outbound

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

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge MUSAN: A Music, Speech, and Noise Corpus

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:51.008961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fa8e89f-cbb3-44ca-af07-5f971f43bb43 · outbound

This paper cites A study on data augmentation of reverberant speech for robust speech recognition,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge A study on data augmentation of reverberant speech for robust speech recognition,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:51.013963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 000b6de7-7174-43f9-938d-cf437e808862 · outbound

This paper cites Decoupled Weight Decay Regularization.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Decoupled Weight Decay Regularization

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:51.018355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0860dbf-586f-42bb-a1c1-4c465b1f3df6 · outbound

This paper cites X-vectors: Robust dnn embeddings for speaker recognition,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge X-vectors: Robust dnn embeddings for speaker recognition,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.243076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ec756052-af06-46ed-91f8-d6701f30ab20 · outbound

This paper cites The idlab voxsrc-20 submission: Large margin fine-tuning and quality- aware score calibration in dnn based speaker verification,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge The idlab voxsrc-20 submission: Large margin fine-tuning and quality- aware score calibration in dnn based speaker verification,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.227225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 55a9feaf-8962-4be4-ac81-e7a445c6af10 · outbound

This paper cites V oxceleb: A large- scale speaker identification dataset,.

Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge V oxceleb: A large- scale speaker identification dataset,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:51.213125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 0de7e9ae-52dc-4b9d-8d73-48d9d8f1c991 · inbound