Paper Citation Record · LEDGER

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning

As of 19 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2504.18582.

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

pith.paper-citation-record.v1
2504.18582 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:02:45.295621Z

measured 62 of 62 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T22:11:44.891731Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

61 of 61 outbound references displayed

verified exact4
verified fuzzy43
unresolved13
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 2eb515d7-8143-4cbf-8543-c645585f80df · outbound

This paper cites This work has gained significant importance in the field of speech processing.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning This work has gained significant importance in the field of speech processing

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:47.256123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a8314ec0-d525-4c8b-8933-f2e1ed82fd46 · outbound

This paper cites an unresolved cited work.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:02:47.227098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93e919b9-3a82-4ce8-ac0d-cb41589d4907 · outbound

This paper cites an unresolved cited work.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:02:47.203287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91d64bf9-75f7-44ee-808e-9c96e65c1b1b · outbound

This paper cites Finally, Conclusion and Future Work.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Finally, Conclusion and Future Work

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:47.181702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation afae7ea1-6d3d-4fd7-8939-5d7850214b0a · outbound

This paper cites data augmentation.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning data augmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:47.157973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 42b3e2f8-65f7-4a91-9472-27147b42d0fa · outbound

This paper cites The approach starts by providing a comprehensive depiction of the dataset, including its organization and the preprocessing procedures executed to make it suitable for training.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning The approach starts by providing a comprehensive depiction of the dataset, including its organization and the preprocessing procedures executed to make it suitable for training

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:47.134988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aad3470c-30c0-451d-b8c5-7f55865bf378 · outbound

This paper cites Ensuring the model's ability to differentiate between distinct voices was crucial, especially for recordings involving many speakers [41].

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Ensuring the model's ability to differentiate between distinct voices was crucial, especially for recordings involving many speakers [41]

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:47.109660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b0c17a5d-7989-428d-b0d3-e6f2d0dc1c81 · outbound

This paper cites By normalizing the data, the model is able to prioritize the distinct attributes of each speaker's voice, without being affected by differences in volume [42].

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning By normalizing the data, the model is able to prioritize the distinct attributes of each speaker's voice, without being affected by differences in volume [42]

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:47.078930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 377f9d20-210e-4c19-adc9-68f06f6d12b9 · outbound

This paper cites Segmentation aids in the training of the model to identify shifts in speakers and enhances its capacity to process lengthy audio re cordings [1].

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Segmentation aids in the training of the model to identify shifts in speakers and enhances its capacity to process lengthy audio re cordings [1]

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:47.051234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8530449a-c15b-4b19-8f04-0f986ede9e81 · outbound

This paper cites These strategies enhance the model's resilience to various acoustic circumstances and speaker varianc es [43].

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning These strategies enhance the model's resilience to various acoustic circumstances and speaker varianc es [43]

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:47.020538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f238edb9-5f57-4c99-ada2-e017c3ca4607 · outbound

This paper cites an unresolved cited work.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:02:46.995381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 916cb460-6451-4af6-9a5c-14d0cc408d76 · outbound

This paper cites an unresolved cited work.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:02:46.974572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ad37cf4d-a1c7-4986-afb7-1e0abe3d33b3 · outbound

This paper cites This change really considers practical situations where speakers may speak at different tempos in order to enhance the model for variation in time.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning This change really considers practical situations where speakers may speak at different tempos in order to enhance the model for variation in time

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.950037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c6285a77-4634-40a3-909a-681d9017df84 · outbound

This paper cites The initial learning rate was fixed at 1e -5 as set by previous experiments and adjusted with a constant cosine rate to obtain convergence.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning The initial learning rate was fixed at 1e -5 as set by previous experiments and adjusted with a constant cosine rate to obtain convergence

Reference 14

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T11:02:46.924965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 908e1fec-98c5-4cd6-9c06-374586cb29f7 · outbound

This paper cites an unresolved cited work.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:02:46.900849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 797d4d03-069a-4225-a87c-c6aa57f65707 · outbound

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

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning A review of speaker diarization: Recent advances with deep learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.880861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 23b5f647-b18e-484f-b359-787adc061e73 · outbound

This paper cites End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T11:02:44.924355Z

Source-reported events for the cited work

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Observation a7c2dfbd-21bd-4f57-8334-6eb68e0bca9c · outbound

This paper cites Language and Speech Technology for Central Kurdish Varieties.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Language and Speech Technology for Central Kurdish Varieties

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:02:45.743380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ebc924b-62b1-4bcf-a120-a269ab762567 · outbound

This paper cites KuBERT: Central Kurdish BERT Model and Its Application for Sentiment Analysis,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning KuBERT: Central Kurdish BERT Model and Its Application for Sentiment Analysis,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.858862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 26fb66ae-5d77-4946-8a0d-c612e634bb73 · outbound

This paper cites wav2vec 2.0: A framework for self -supervised learning of speech representations,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning wav2vec 2.0: A framework for self -supervised learning of speech representations,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.838612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1a6f2899-cca5-46c4-9a03-c33486018c95 · outbound

This paper cites wav2vec: Unsupervised Pre-training for Speech Recognition.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning wav2vec: Unsupervised Pre-training for Speech Recognition

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T11:02:44.973306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60c6b99f-add4-4567-9527-bf4a62662ee0 · outbound

This paper cites Breaking Walls: Pioneering Automatic Speech Recognition for Central Kurdish: End-to-End Transformer Paradigm.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Breaking Walls: Pioneering Automatic Speech Recognition for Central Kurdish: End-to-End Transformer Paradigm

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:02:45.682821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ab0895dc-91e1-45f2-8a06-475dd8da7ec3 · outbound

This paper cites MLS: A Large-Scale Multilingual Dataset for Speech Research.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning MLS: A Large-Scale Multilingual Dataset for Speech Research

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:02:45.012822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f46ace2e-1554-410f-b9ed-7e4ab603cc3a · outbound

This paper cites Deep Learning for Natural Language Processing in Low -Resource Languages,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Deep Learning for Natural Language Processing in Low -Resource Languages,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.819976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a524bea6-a8fc-4868-9448-0a37a570b069 · outbound

This paper cites A survey on text classification: From traditional to deep learning,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning A survey on text classification: From traditional to deep learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.791280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 263e43c5-37b4-4112-89a1-6b137a903c29 · outbound

This paper cites A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:02:45.036126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0463ba69-c635-424a-95a7-316b376e0608 · outbound

This paper cites Central Kurdish Automatic Speech Recognition using Deep Learning,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Central Kurdish Automatic Speech Recognition using Deep Learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.770633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 598ac89c-81e6-4180-962a-664b5bf9f289 · outbound

This paper cites Enhancing speaker diarization with large language models: A contextual beam search approach,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Enhancing speaker diarization with large language models: A contextual beam search approach,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.747285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 54a77b2a-db78-4133-ab7a-cf962dba7d29 · outbound

This paper cites 2019 YEAR IN REVIEW: MACHINE LEARNING IN HEALTHCARE,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning 2019 YEAR IN REVIEW: MACHINE LEARNING IN HEALTHCARE,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.722856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4f996201-96ed-4be9-b459-65df95d2e2ed · outbound

This paper cites Speaker diarization: A review of recent research,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Speaker diarization: A review of recent research,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.698177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d829861e-eb41-4eff-be62-b0530ef2a4c0 · outbound

This paper cites Approaches and applications of audio diarization,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Approaches and applications of audio diarization,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.673655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 63654576-0401-4471-8554-6b448b961cd9 · outbound

This paper cites Speaker diarization with PLDA i-vector scoring and unsupervised calibration,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Speaker diarization with PLDA i-vector scoring and unsupervised calibration,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.644618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4a0aebe7-201a-48de-a762-b1a959ecb8ef · outbound

This paper cites Speaker diarization with LSTM,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Speaker diarization with LSTM,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.625972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dd8eb248-529a-4a8d-b38d-17d29bd67ee3 · outbound

This paper cites FocusNet: imbalanced large and small organ segmentation with an end -to-end deep neural network for head and neck CT images,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning FocusNet: imbalanced large and small organ segmentation with an end -to-end deep neural network for head and neck CT images,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.604307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 26873f2f-37a2-439c-a622-11b828ef5a69 · outbound

This paper cites End -to-end neural speaker diarization with self-attention,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning End -to-end neural speaker diarization with self-attention,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.569184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c3684d9b-e1ad-47f1-b58f-f6c8023b4edf · outbound

This paper cites The Third DIHARD Diarization Challenge.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning The Third DIHARD Diarization Challenge

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T11:02:45.119657Z

Source-reported events for the cited work

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Observation fcc2cb36-56df-411a-9ae4-9c78222834cb · outbound

This paper cites Automatic speech recognition for under -resourced languages: A survey,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Automatic speech recognition for under -resourced languages: A survey,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.529282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 20f9abba-355a-4e50-a32e-f73996aa8f84 · outbound

This paper cites Advances in Deep Speaker Verification: a study on robustness, portability, and security,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Advances in Deep Speaker Verification: a study on robustness, portability, and security,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.504126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 193f301f-b27e-4475-b444-378f4744168a · outbound

This paper cites Towards end -to-end speaker diarization with generalized neural speaker clustering,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Towards end -to-end speaker diarization with generalized neural speaker clustering,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.471010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 60c153b0-9fd3-45ac-93c0-bb0e815b9d3c · outbound

This paper cites Equity Impacts of Dollar Store Vaccine Distribution.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Equity Impacts of Dollar Store Vaccine Distribution

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:02:45.446006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 62b674be-9213-4349-a18d-f6addc78eaca · outbound

This paper cites Kurdish interdialect machine translation,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Kurdish interdialect machine translation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.445377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cf12cdd8-180e-44f9-a90b-905eaefee436 · outbound

This paper cites Jira: a Central Kurdish speech recognition system, designing and building speech corpus and pronunciation lexicon,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Jira: a Central Kurdish speech recognition system, designing and building speech corpus and pronunciation lexicon,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.423290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d0aea66f-bb6b-4a8e-a003-8cfc67715e2e · outbound

This paper cites Kurdish dialect recognition using 1D CNN,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Kurdish dialect recognition using 1D CNN,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.400779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0ba15c5d-3610-433b-ae78-1e0a0ee22b6c · outbound

This paper cites Effectiveness of self -supervised pre-training for asr,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Effectiveness of self -supervised pre-training for asr,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.381125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5c037890-6ca6-439e-bd9a-b53a6cb41cb8 · outbound

This paper cites Exploring wav2vec 2.0 on speaker verification and language identification.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Exploring wav2vec 2.0 on speaker verification and language identification

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T11:02:45.184624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31932d37-cfd9-438a-a736-2f7e99c13b8d · outbound

This paper cites EEND-SS: Joint end-to-end neural speaker diarization and speech separation for flexible number of speakers,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning EEND-SS: Joint end-to-end neural speaker diarization and speech separation for flexible number of speakers,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.183388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d90d1aa-2357-4c04-a9a1-40b37869c19c · outbound

This paper cites MSFNet: Multi-Scale Fusion Network for Brain -Controlled Speaker Extraction,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning MSFNet: Multi-Scale Fusion Network for Brain -Controlled Speaker Extraction,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.149526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 750af1d8-cc94-4e41-96f2-3612f7c2ba95 · outbound

This paper cites Unsupervised Cross-lingual Representation Learning for Speech Recognition.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Unsupervised Cross-lingual Representation Learning for Speech Recognition

Reference 49

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Observation d1686c16-39d3-4d4c-bf0d-fa5be58ec367 · outbound

This paper cites A survey on transfer learning,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning A survey on transfer learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.122331Z

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Observation adc4ae1e-f191-4032-9676-dfc275e1a9d5 · outbound

This paper cites A Survey on Transfer Learning in Natural Language Processing.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning A Survey on Transfer Learning in Natural Language Processing

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T11:02:45.226247Z

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Observation 58396a1e-17ff-48ff-8e54-42c707ecf82a · outbound

This paper cites The NIST speaker recognition evaluation program,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning The NIST speaker recognition evaluation program,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.094437Z

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Observation 5315e7ee-620a-4f24-b4d4-eaaa95af1b29 · outbound

This paper cites NSGA-II-DL: Metaheuristic optimal feature selection with Deep Learning Framework for HER2 classification in Breast Cancer,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning NSGA-II-DL: Metaheuristic optimal feature selection with Deep Learning Framework for HER2 classification in Breast Cancer,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.069478Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3d4e4e14-876e-48d8-a264-c1fb27d5449d · outbound

This paper cites Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.041674Z

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Observation 336b51ff-8e4f-4c3e-9fb6-0ec71b6a040e · outbound

This paper cites Towards Unsupervised Speaker Diarization System for Multilingual Telephone Calls Using Pre-trained Whisper Model and Mixture of Sparse Autoencoders.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Towards Unsupervised Speaker Diarization System for Multilingual Telephone Calls Using Pre-trained Whisper Model and Mixture of Sparse Autoencoders

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:02:45.541329Z

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Observation 7e0038d7-a6c0-4b8a-ba35-d1b9261bb7d9 · outbound

This paper cites Audacity (R): Free audio editor and recorder [Computer application]. Version 3.0. 0 retrieved March 17th, 2021,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Audacity (R): Free audio editor and recorder [Computer application]. Version 3.0. 0 retrieved March 17th, 2021,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:46.009047Z

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Observation 6c62941f-bd99-4c8a-9378-dd7c71d7b6a5 · outbound

This paper cites Praat: doing phonetics by computer [Computer program],.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Praat: doing phonetics by computer [Computer program],

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:45.983477Z

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Observation 6da349ea-d173-4231-84bd-e75d96ed07a0 · outbound

This paper cites Audio augmentation for speech recognition,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Audio augmentation for speech recognition,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:45.942794Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e8b710ed-84e4-4d74-b5dd-d9a9ddb2ba7a · outbound

This paper cites Improving language understanding by generative pre -training,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Improving language understanding by generative pre -training,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:45.910867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 859475a3-0141-459f-8dd1-601ff34ca817 · outbound

This paper cites Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:45.880305Z

Source-reported events for the cited work

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Observation f1e0e054-0469-43ee-bf53-945cf134f878 · outbound

This paper cites Topic segmentation with an aspect hidden Markov model,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Topic segmentation with an aspect hidden Markov model,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:45.849828Z

Source-reported events for the cited work

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Observation a7153bfd-b60d-425f-812f-a0b956d43022 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting,.

Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning Dropout: a simple way to prevent neural networks from overfitting,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:02:45.816852Z

Source-reported events for the cited work

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Pith citing papers

Observation 33d140e9-adf6-41f4-8b1e-bcd7f04b8fcb · inbound

Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents cites this paper.

Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning

Reference 245

Resolution: verified exact; arxiv_id, observed 2026-06-30T22:15:05.584407Z

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