Paper Citation Record · LEDGER

The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties

As of 20 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.

pith.paper-citation-record.v1
2509.07139 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

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measured 64 of 64 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T16:16:38.122462Z

Reference resolution

63 of 63 outbound references displayed

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External citation measurements

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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

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The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Unresolved cited work

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The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Unresolved cited work

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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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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

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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

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The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Unresolved cited work

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The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties Unresolved cited work

Reference 10

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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: verified fuzzy; raw_fallback, observed 2026-08-15T16:16:38.454565Z

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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

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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

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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; raw_fallback, observed 2026-08-15T16:16:38.422863Z

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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; raw_fallback, observed 2026-08-15T16:16:38.406998Z

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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; raw_fallback, observed 2026-08-15T16:16:38.392299Z

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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: verified fuzzy; raw_fallback, observed 2026-08-15T16:16:38.378549Z

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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

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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; raw_fallback, observed 2026-08-15T16:16:38.349812Z

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Observation ae9fd73c-ce44-41c7-8dda-3b529975b920 · outbound

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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

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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

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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

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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

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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

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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

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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

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Pith citing papers

Observation fc5f675d-e757-4a8f-bc63-55d4076eef8b · inbound