Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T16:16:37.910207Z
Paper Citation Record · LEDGER
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties
As of 16 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2509.07139.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T16:16:37.636867Z
A source-named dated measurement, never combined with another source.
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Reference resolution
63 of 63 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation de631d99-9ca2-4449-a5a9-ee81d2276630 · outbound
This paper cites standard.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties standard
Reference 1
- Resolution
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Observation 4c143fba-48f0-4b81-8ac4-699ecb710f7f · outbound
This paper cites an unresolved cited work.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Unresolved cited work
Reference 2
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Observation ab223d1e-6abf-4c94-a331-86a6f31dd1c1 · outbound
This paper cites an unresolved cited work.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Unresolved cited work
Reference 3
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Observation 484c9fc6-d768-4763-80cc-2a0fc4f251c2 · outbound
This paper cites an unresolved cited work.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Unresolved cited work
Reference 4
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Observation fc5f675d-e757-4a8f-bc63-55d4076eef8b · outbound
This paper cites The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties
Reference 5
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Observation c41d9204-4998-474f-9a91-c92977c7133f · outbound
This paper cites The hidden test set con- tains data sourced from the same corpora as the development set along with 4 additional corpora [34–37].
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties The hidden test set con- tains data sourced from the same corpora as the development set along with 4 additional corpora [34–37]
Reference 6
- Resolution
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Source-reported events for the cited work
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Observation 66257566-f0e3-4e73-b8fa-7148c570d7f0 · outbound
This paper cites "" 3Args: 4waveform (np.array): speech waveform 5Returns: 6pred_lid (str): ISO3 code of LID pred 7pred_asr (str): predicted transcript 8.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties "" 3Args: 4waveform (np.array): speech waveform 5Returns: 6pred_lid (str): ISO3 code of LID pred 7pred_asr (str): predicted transcript 8
Reference 7
- Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bd911933-4b36-405b-8b7f-d1721a68ee2e · outbound
This paper cites an unresolved cited work.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Unresolved cited work
Reference 8
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Observation 16f95097-d7ec-4f5c-80bd-ee5e943a54dd · outbound
This paper cites an unresolved cited work.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Unresolved cited work
Reference 9
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Observation 7774eeea-b64c-469a-aff3-6574f5ab43d6 · outbound
This paper cites an unresolved cited work.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Unresolved cited work
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation e663b56b-c5b0-492e-95e3-90e3964986d2 · outbound
This paper cites Since all of these models are self-supervised, we develop ASR systems via fine-tuning on the ML-SUPERB 2.0 public set [14].
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Since all of these models are self-supervised, we develop ASR systems via fine-tuning on the ML-SUPERB 2.0 public set [14]
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation 6dc5ea14-434b-4d58-b09b-85a79daee2c4 · outbound
This paper cites Superb@ slt 2022: Challenge on general- ization and efficiency of self-supervised speech representation learning,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Superb@ slt 2022: Challenge on general- ization and efficiency of self-supervised speech representation learning,
Reference 12
- Resolution
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Observation 040a659d-2eb0-4eed-aa79-2f32009a5521 · outbound
This paper cites The challenge introduces a novel multilin- gual test suite of accented and dialect speech and uses new metrics to test the robustness of ASR systems.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties The challenge introduces a novel multilin- gual test suite of accented and dialect speech and uses new metrics to test the robustness of ASR systems
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation b3bdd008-6e1c-4f7d-9e7c-e7cc5c0bc48e · outbound
This paper cites Wav2vec 2.0: A framework for self- supervised learning of speech representations,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Wav2vec 2.0: A framework for self- supervised learning of speech representations,
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation d3032ef4-ea2d-4f59-afb3-6a92517e6de6 · outbound
This paper cites Hubert: Self-supervised speech representation learning by masked prediction of hidden units,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Hubert: Self-supervised speech representation learning by masked prediction of hidden units,
Reference 15
- Resolution
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Observation d3e44ed5-3409-4bd6-a470-77a2b3bc2150 · outbound
This paper cites Robust speech recognition via large-scale weak supervision,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Robust speech recognition via large-scale weak supervision,
Reference 16
- Resolution
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Source-reported events for the cited work
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Observation ddcc2970-6156-4895-bb1e-c36b72429415 · outbound
This paper cites OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation f3b62685-fee0-4190-a213-962adf70e875 · outbound
This paper cites Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages
Reference 18
- Resolution
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Source-reported events for the cited work
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Observation 1f3ccd39-f221-4734-950c-75ef6d178936 · outbound
This paper cites Self-supervised speech representations still struggle with african american vernacular english,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Self-supervised speech representations still struggle with african american vernacular english,
Reference 19
- Resolution
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Observation fab1cb7d-47a0-457f-9813-411e708a92cd · outbound
This paper cites Towards inclusive automatic speech recognition,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Towards inclusive automatic speech recognition,
Reference 20
- Resolution
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Source-reported events for the cited work
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Observation 1bbf7d44-e0a1-479f-8b9c-19062dcd11e6 · outbound
This paper cites Findings of the IWSLT 2023 Evaluation Campaign,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Findings of the IWSLT 2023 Evaluation Campaign,
Reference 21
- Resolution
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Source-reported events for the cited work
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Observation d35adab2-e89b-40e4-8204-38b790dd0916 · outbound
This paper cites SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Genera- tive Capabilities,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Genera- tive Capabilities,
Reference 22
- Resolution
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Source-reported events for the cited work
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Observation 4d1636ec-4af0-4d45-a3fd-698b3687bef9 · outbound
This paper cites A V-SUPERB: A Multi-Task Evaluation Bench- mark for Audio-Visual Representation Models,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties A V-SUPERB: A Multi-Task Evaluation Bench- mark for Audio-Visual Representation Models,
Reference 23
- Resolution
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Observation a26f6842-2a32-43a5-9a56-9ad62c1ab07a · outbound
This paper cites ML-SUPERB: Multilingual Speech Universal PERformance Benchmark,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties ML-SUPERB: Multilingual Speech Universal PERformance Benchmark,
Reference 24
- Resolution
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Observation d7b00ff8-0310-4c95-be99-b99aa9e88013 · outbound
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The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Sada: Saudi audio dataset for arabic,
Reference 25
- Resolution
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Observation ee291495-6a38-48e1-bfd0-33077e4cd356 · outbound
This paper cites Findings of the 2023 ML-SUPERB Challenge: Pre- Training And Evaluation Over More Languages And Beyond,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Findings of the 2023 ML-SUPERB Challenge: Pre- Training And Evaluation Over More Languages And Beyond,
Reference 26
- Resolution
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Observation ad19c228-ce3d-45b3-8118-d0a9da88ee82 · outbound
This paper cites ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets,
Reference 27
- Resolution
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Observation 210af595-383f-4979-971c-89616d36f716 · outbound
This paper cites SUPERB: Speech Processing Universal PERfor- mance Benchmark,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties SUPERB: Speech Processing Universal PERfor- mance Benchmark,
Reference 28
- Resolution
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Observation 66f6b343-8b93-42dc-8375-ef7d438da6a3 · outbound
This paper cites Dynabench: Rethinking benchmarking in NLP,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Dynabench: Rethinking benchmarking in NLP,
Reference 29
- Resolution
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Observation caba7e39-0f54-4651-9fbc-8adef189c600 · outbound
This paper cites Scaling speech technology to 1,000+ lan- guages,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Scaling speech technology to 1,000+ lan- guages,
Reference 30
- Resolution
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Source-reported events for the cited work
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Observation 4780c053-c0f3-4e61-8c0d-5fb0a35c4a62 · outbound
This paper cites Towards Robust Speech Representation Learning for Thousands of Languages.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Towards Robust Speech Representation Learning for Thousands of Languages
Reference 31
- Resolution
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Source-reported events for the cited work
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Observation 3ff66918-3ebb-44f4-839b-37366a819b4a · outbound
This paper cites Artie bias corpus: An open dataset for detecting demographic bias in speech applications,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Artie bias corpus: An open dataset for detecting demographic bias in speech applications,
Reference 32
- Resolution
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Observation 141c0f25-709a-41df-b99f-b907eb0ebcb2 · outbound
This paper cites Speech Accent Archive,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Speech Accent Archive,
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation 82cb4b4c-9e18-424e-ba87-cec770c2e4d2 · outbound
This paper cites Towards measuring fairness in speech recognition: Fair-Speech dataset.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Towards measuring fairness in speech recognition: Fair-Speech dataset
Reference 34
- Resolution
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Source-reported events for the cited work
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Observation 96c7fe15-902d-44ab-89cc-1078fef94ad6 · outbound
This paper cites Fleurs: Few-shot learning evaluation of uni- versal representations of speech,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Fleurs: Few-shot learning evaluation of uni- versal representations of speech,
Reference 35
- Resolution
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Source-reported events for the cited work
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Observation 5dc9c1b3-466f-4b00-a96f-4c714bef1769 · outbound
This paper cites Common voice: A massively-multilingual speech corpus,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Common voice: A massively-multilingual speech corpus,
Reference 36
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 81eaba92-bba0-4f4e-850b-941f4a837efa · outbound
This paper cites SeamlessM4T: Massively Multilingual & Multimodal Machine Translation.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
Reference 37
- Resolution
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Source-reported events for the cited work
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Observation 2d8a4589-7bc8-40df-b684-099efb44ff2a · outbound
This paper cites Findings of the WMT 2021 shared task on large-scale multilingual machine translation,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Findings of the WMT 2021 shared task on large-scale multilingual machine translation,
Reference 38
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8df50f6e-fa5f-40e9-814a-4f98634bde35 · outbound
This paper cites V oxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties V oxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation,
Reference 39
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 426972c6-5bb2-4161-bcf0-1b8c73964a97 · outbound
This paper cites Open-source multi-speaker corpora of the English accents in the British isles,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Open-source multi-speaker corpora of the English accents in the British isles,
Reference 40
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 566d2070-7066-4352-9c47-5f782c6e98e2 · outbound
This paper cites Globe: A high-quality english corpus with global accents for zero-shot speaker adaptive text- to-speech,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Globe: A high-quality english corpus with global accents for zero-shot speaker adaptive text- to-speech,
Reference 41
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 16d19aa3-28c8-4a9d-9ca1-40d6c1869f00 · outbound
This paper cites L2-arctic: A non-native english speech corpus,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties L2-arctic: A non-native english speech corpus,
Reference 42
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fb957ccc-ca98-42e2-8eb7-a723267e2295 · outbound
This paper cites Dogan-Schönberger, J.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Dogan-Schönberger, J
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.364857Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 113b992d-d453-4366-810e-0e15f6be9ba4 · outbound
This paper cites Speech recognition for greek dialects: A challenging benchmark,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Speech recognition for greek dialects: A challenging benchmark,
Reference 44
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 756b7290-361a-4ce1-a857-c735b4013eb7 · outbound
This paper cites Interspeech 2018 low resource au- tomatic speech recognition challenge for indian languages,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Interspeech 2018 low resource au- tomatic speech recognition challenge for indian languages,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.335751Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3237c8af-a348-487a-9fb7-8542319aa2c6 · outbound
This paper cites Crowdsourcing Latin American Span- ish for low-resource text-to-speech,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Crowdsourcing Latin American Span- ish for low-resource text-to-speech,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.320731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 444f61a2-fc90-4bf1-8d39-7de5ecae4bec · outbound
This paper cites Leveraging data collection and un- supervised learning for code-switched tunisian arabic automatic speech recognition,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Leveraging data collection and un- supervised learning for code-switched tunisian arabic automatic speech recognition,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.304645Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bdcd86b3-edeb-4955-be61-50a719fc68b7 · outbound
This paper cites Casablanca: Data and Models for Multidialectal Arabic Speech Recognition.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Casablanca: Data and Models for Multidialectal Arabic Speech Recognition
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:16:37.839473Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c54f854d-93c8-4ae3-a590-b931254fb5f4 · outbound
This paper cites Automatic speech recognition datasets in Can- tonese: A survey and new dataset,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Automatic speech recognition datasets in Can- tonese: A survey and new dataset,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.289486Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5eb172f4-2759-4fd3-afdb-c32abf1f1807 · outbound
This paper cites These are run in azero-shot manner, as they are designed to be used out-of-the-box.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties These are run in azero-shot manner, as they are designed to be used out-of-the-box
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.752004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ff04132a-89b9-4531-8f7e-76eb4ac7991f · outbound
This paper cites Finnish dialect identification: The effect of audio and text,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Finnish dialect identification: The effect of audio and text,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.274479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 73107445-82a0-4021-88b5-2f0b641d661f · outbound
This paper cites Findings of the 2021 conference on ma- chine translation (WMT21),.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Findings of the 2021 conference on ma- chine translation (WMT21),
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.244629Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 83a6f4bc-46bb-4f7f-952a-4a485d8848b6 · outbound
This paper cites OWSM v3.1: Better and Faster Open Whisper- Style Speech Models based on E-Branchformer,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties OWSM v3.1: Better and Faster Open Whisper- Style Speech Models based on E-Branchformer,
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.227954Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f7495814-cf0d-455d-aca0-b531c1878b76 · outbound
This paper cites LLaMA: Open and Efficient Foundation Language Models.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties LLaMA: Open and Efficient Foundation Language Models
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:16:37.866927Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9cf07fb7-661b-4a4d-befb-a63d7adc2869 · outbound
This paper cites Language models are few-shot learners,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Language models are few-shot learners,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.211147Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 48e633f2-cf7b-4a39-80ea-52a96ea96596 · outbound
This paper cites The Interspeech 2024 Challenge on Speech Processing Using Discrete Units.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties The Interspeech 2024 Challenge on Speech Processing Using Discrete Units
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:16:37.877180Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ae9fd73c-ce44-41c7-8dda-3b529975b920 · outbound
This paper cites NeurIPS 2024 competition proposal: UR- GENT challenge,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties NeurIPS 2024 competition proposal: UR- GENT challenge,
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.195572Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8c773e1b-01a4-487d-87eb-c35a8b3a8701 · outbound
This paper cites Wavlm: Large-scale self-supervised pre-training for full stack speech processing,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Wavlm: Large-scale self-supervised pre-training for full stack speech processing,
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.178873Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 76d1ea6f-c6b8-4d33-a5c3-d19525a93f52 · outbound
This paper cites Unsupervised Cross-lingual Representation Learning for Speech Recognition.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Unsupervised Cross-lingual Representation Learning for Speech Recognition
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:16:37.890944Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 70ccc7c0-ae92-4041-83a6-1d4010e5e8a6 · outbound
This paper cites XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:16:37.895958Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 05e9e296-7b1d-4692-a060-cc0644f9f303 · outbound
This paper cites Attention is all you need,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Attention is all you need,
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.163113Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0ab2d292-5149-4ec4-ba40-f5709faf45e4 · outbound
This paper cites Connectionist temporal classification: La- belling unsegmented sequence data with recurrent neural net- works,.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Connectionist temporal classification: La- belling unsegmented sequence data with recurrent neural net- works,
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:16:38.147350Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d504821c-e3a8-40a7-a572-40123c91faf5 · outbound
This paper cites OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:16:37.910207Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation fc5f675d-e757-4a8f-bc63-55d4076eef8b · inbound
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties cites this paper.
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties
Reference 5
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-15T16:16:38.130032Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.