Paper Citation Record · LEDGER

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR

As of 9 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2505.24656.

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

pith.paper-citation-record.v1
2505.24656 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:20:32.374049Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:20:29.013895Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:20:32.461097Z

Reference resolution

35 of 35 outbound references displayed

verified exact1
verified fuzzy33
unresolved1
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 780b23e6-4706-4992-a7aa-da90dc12ba65 · outbound

This paper cites MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:20:32.518193Z

Source-reported events for the cited work

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Observation e78fffe2-b38b-47e9-b4cb-1f7c6e5d1fb7 · outbound

This paper cites 1 illustrates our proposed approach, which builds on and extends the methodologies presented in [19] and [11].

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR 1 illustrates our proposed approach, which builds on and extends the methodologies presented in [19] and [11]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:38.275511Z

Source-reported events for the cited work

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Observation 8714e3de-9ad1-4b73-979e-393753b72e40 · outbound

This paper cites Pre-trained model: For our base model, we utilize XLSR-53 [23] , a state-of-the-art pre-trained speech model developed on the Wav2Vec 2.0 [20] architecture.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Pre-trained model: For our base model, we utilize XLSR-53 [23] , a state-of-the-art pre-trained speech model developed on the Wav2Vec 2.0 [20] architecture

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:37.969286Z

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Observation 7ff0abbd-f69e-453a-a25c-b7ef8737e303 · outbound

This paper cites an unresolved cited work.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Unresolved cited work

Reference 4

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Observation fd4870f5-8d23-448e-b0e9-9cbb0bd56d2c · outbound

This paper cites Teacher” col- umn contains teacher’s original WER on target domain, while the “Student.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Teacher” col- umn contains teacher’s original WER on target domain, while the “Student

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:37.490316Z

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Observation 5368eaac-c601-4478-a5c2-74a68870395f · outbound

This paper cites We found that Meta PL is an ef- fective adaptation method, providing a straightforward and eas- ily implementable solution.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR We found that Meta PL is an ef- fective adaptation method, providing a straightforward and eas- ily implementable solution

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:37.392819Z

Source-reported events for the cited work

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Observation b06ed934-9b0d-4272-aa4a-a0328860dbc1 · outbound

This paper cites Competitiveness.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Competitiveness

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:37.294294Z

Source-reported events for the cited work

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Observation 92b0db06-8677-49e3-86d9-7d612b88e899 · outbound

This paper cites Learning transferable features with deep adapta- tion networks,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Learning transferable features with deep adapta- tion networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:37.200247Z

Source-reported events for the cited work

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Observation 01cd1aea-9eb5-437f-9cf4-cb5ce0266ebc · outbound

This paper cites Domain-adversarial training of neural networks,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Domain-adversarial training of neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:37.094580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4da04f9e-c033-4c32-952f-24f44e69d625 · outbound

This paper cites Unsupervised domain adaptation schemes for building asr in low-resource languages,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Unsupervised domain adaptation schemes for building asr in low-resource languages,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:36.961405Z

Source-reported events for the cited work

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Observation bc8f3695-d683-49a0-860f-79dc652e9952 · outbound

This paper cites Domain adaptation of dnn acoustic models using knowledge distillation,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Domain adaptation of dnn acoustic models using knowledge distillation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:36.775903Z

Source-reported events for the cited work

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Observation e54a7625-e8d0-4baa-a247-059b8192b362 · outbound

This paper cites Probability of error of some adaptive pattern- recognition machines,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Probability of error of some adaptive pattern- recognition machines,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:36.547805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2123c817-fb9e-42b2-b416-3af9148169d4 · outbound

This paper cites Learning extraction patterns for subjec- tive expressions,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Learning extraction patterns for subjec- tive expressions,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:36.290709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 17f5804f-d0a0-4ac6-9eb8-e74529986ef3 · outbound

This paper cites Large-scale asr domain adaptation using self- and semi-supervised learning,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Large-scale asr domain adaptation using self- and semi-supervised learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:36.133206Z

Source-reported events for the cited work

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Observation fb6ae582-c1d8-4642-b36e-eacbdd618619 · outbound

This paper cites Pseudo label is better than human label,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Pseudo label is better than human label,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:35.953225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e379ab2e-5630-4fb2-9215-eb69d3dd7405 · outbound

This paper cites Improved noisy student training for automatic speech recognition,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Improved noisy student training for automatic speech recognition,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:35.756798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 16ac8d20-ed18-4504-a51d-9ea333579513 · outbound

This paper cites Kaizen: Continuously improving teacher using exponential moving average for semi-supervised speech recognition,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Kaizen: Continuously improving teacher using exponential moving average for semi-supervised speech recognition,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:35.599243Z

Source-reported events for the cited work

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Observation 643fe245-d415-4266-b34d-c0a41872f641 · outbound

This paper cites Meta pseudo labels,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Meta pseudo labels,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:35.311159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ba450e2d-4d55-444a-9c05-c6d68b0a29ce · outbound

This paper cites Progressive unsupervised domain adaptation for asr using ensemble models and multi-stage training,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Progressive unsupervised domain adaptation for asr using ensemble models and multi-stage training,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:35.032450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5fc91070-d3c4-46b9-8f26-4d00cf2e1130 · outbound

This paper cites knn-ctc: Enhancing asr via retrieval of ctc pseudo labels,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR knn-ctc: Enhancing asr via retrieval of ctc pseudo labels,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:34.850560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7edc3b54-9b85-4686-9320-ec3b3fe20044 · outbound

This paper cites Udalm: Unsupervised domain adaptation through language modeling,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Udalm: Unsupervised domain adaptation through language modeling,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:34.717573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9b3e60b1-84b0-4b59-bbc6-ccfe72974b31 · outbound

This paper cites Don’t stop pretraining: Adapt language models to domains and tasks,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Don’t stop pretraining: Adapt language models to domains and tasks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:34.568630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c772cc4d-36cf-4ba0-ba2d-c2c1b095de50 · outbound

This paper cites Robust wav2vec 2.0: Analyzing domain shift in self-supervised pre-training,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Robust wav2vec 2.0: Analyzing domain shift in self-supervised pre-training,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:34.443479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 02dea9f6-5062-48cb-bb62-f97a7ba2861a · outbound

This paper cites Boosting cross-domain speech recognition with self-supervision,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Boosting cross-domain speech recognition with self-supervision,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:34.338062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8a19a346-befe-46fa-b73d-d3f7fb017471 · outbound

This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Bert: Pre-training of deep bidirectional trans- formers for language understanding,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:34.190148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 23c51861-9302-4b5b-8d16-c7896a122685 · outbound

This paper cites Sample-efficient unsupervised domain adaptation of speech recognition systems: A case study for mod- ern greek,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Sample-efficient unsupervised domain adaptation of speech recognition systems: A case study for mod- ern greek,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:34.062445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 67e25e88-0545-4ffa-9ee0-5677979f8982 · outbound

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

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:33.931003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4314cee0-c472-46aa-ab27-c0736777678f · outbound

This paper cites Towards end-to-end speech recognition with recurrent neural networks,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Towards end-to-end speech recognition with recurrent neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:33.792911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 65e22d0e-60f8-40ab-ab99-a8a116f63469 · outbound

This paper cites Specaugment: A simple data augmentation method for automatic speech recognition,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Specaugment: A simple data augmentation method for automatic speech recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:33.664166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3402be44-188c-4834-9c2a-2647346e4f7b · outbound

This paper cites Unsupervised cross-lingual representation learning for speech recognition,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Unsupervised cross-lingual representation learning for speech recognition,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:33.532561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 35044d35-86f0-49e9-b98b-183e5a140588 · outbound

This paper cites Large vocabulary continuous speech recogni- tion in greek: corpus and an automatic dictation system,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Large vocabulary continuous speech recogni- tion in greek: corpus and an automatic dictation system,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:33.413931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4e8fa44f-07a1-4436-8fef-45e00040c4f1 · outbound

This paper cites Common voice: A massively-multilingual speech corpus,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Common voice: A massively-multilingual speech corpus,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:33.239050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6566134b-57da-4bf7-a0e8-aaf19941336e · outbound

This paper cites The greek podcast corpus: Competi- tive speech models for low-resourced languages with weakly su- pervised data,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR The greek podcast corpus: Competi- tive speech models for low-resourced languages with weakly su- pervised data,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:33.052833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e52ad7eb-b6b0-46b9-ad09-30a21e5d1d2b · outbound

This paper cites Whisperx: Time-accurate speech transcription of long-form audio,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Whisperx: Time-accurate speech transcription of long-form audio,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:32.901782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d6e925b0-a9a6-4939-ade8-aa615ce6e935 · outbound

This paper cites Decoupled weight decay regulariza- tion,.

MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR Decoupled weight decay regulariza- tion,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:20:32.698154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 780b23e6-4706-4992-a7aa-da90dc12ba65 · inbound