Paper Citation Record · LEDGER

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC

As of 8 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2505.24200.

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

pith.paper-citation-record.v1
2505.24200 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:35:02.533642Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:34:58.940094Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:35:02.664010Z

Reference resolution

40 of 40 outbound references displayed

verified exact1
verified fuzzy30
unresolved8
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1e948f24-0805-4c5c-a762-8ca0913a58fd · outbound

This paper cites an unresolved cited work.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:35:09.123181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 28b76465-4680-4ec2-938a-3609ff4e7429 · outbound

This paper cites an unresolved cited work.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:35:08.828683Z

Source-reported events for the cited work

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Observation 944ae7c7-580d-44d4-9f5b-a430229bb551 · outbound

This paper cites Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC

Reference 3

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Observation 513df0b2-18e6-4c60-bd3f-86289e8e1053 · outbound

This paper cites 2 Models are evaluated on the development sets due to the unavailability of the test set.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC 2 Models are evaluated on the development sets due to the unavailability of the test set

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:08.509815Z

Source-reported events for the cited work

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Observation 1f2b0059-b8fc-4653-b7c1-f1b89a86b494 · outbound

This paper cites Training strategies with multilingual SFMs Table 1 shows LID and ASR results across SFMs and training strategies on ML-SUPERB 2.0.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Training strategies with multilingual SFMs Table 1 shows LID and ASR results across SFMs and training strategies on ML-SUPERB 2.0

Reference 5

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Source-reported events for the cited work

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Observation 4ff96bd4-f962-473a-9397-c4be9beb7a08 · outbound

This paper cites We evalu- ate MMS, XEUS, and OWSM-CTC under downstream training with frozen upstream, upstream fine-tuning, and LoRA.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC We evalu- ate MMS, XEUS, and OWSM-CTC under downstream training with frozen upstream, upstream fine-tuning, and LoRA

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:08.255806Z

Source-reported events for the cited work

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Observation 913ec992-d565-4e1f-a979-6fba8c805e34 · outbound

This paper cites an unresolved cited work.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Unresolved cited work

Reference 7

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Observation 6998a5c3-c91d-4b82-a565-2b98ccce63d7 · outbound

This paper cites an unresolved cited work.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Unresolved cited work

Reference 8

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Source-reported events for the cited work

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Observation cd5324fc-53cd-44bf-ba12-b0c8911c8edf · outbound

This paper cites XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:07.838636Z

Source-reported events for the cited work

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Observation c32267e7-16e6-456d-8b30-32d1e41cbece · outbound

This paper cites ASR2K: Speech recognition for around 2000 languages without audio,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC ASR2K: Speech recognition for around 2000 languages without audio,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:07.726772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bb873ce9-86db-4644-9ed2-ed59a7340d98 · outbound

This paper cites Joint prediction and denoising for large-scale multilingual self-supervised learning,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Joint prediction and denoising for large-scale multilingual self-supervised learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:07.579113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5331b52b-a069-4077-8802-b366eb3ece36 · outbound

This paper cites Scaling speech technology to 1,000+ languages,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Scaling speech technology to 1,000+ languages,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:07.460116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ff82f576-bbf1-4bc2-8ca4-b6020e0a0d37 · outbound

This paper cites Towards robust speech representation learning for thousands of languages,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Towards robust speech representation learning for thousands of languages,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:07.301390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 17be225e-280c-4e9f-84cc-e481d8b4c899 · outbound

This paper cites mHuBERT- 147: A compact multilingual HuBERT model,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC mHuBERT- 147: A compact multilingual HuBERT model,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:07.150268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a9a2bdab-663e-4b7e-91df-3d8bcbb7cfb4 · outbound

This paper cites A configurable multilingual model is all you need to recognize all languages,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC A configurable multilingual model is all you need to recognize all languages,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:07.031401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c093eb10-53aa-4999-83b6-9cedeab33a56 · outbound

This paper cites Robust speech recognition via large-scale weak supervision,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Robust speech recognition via large-scale weak supervision,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:06.819589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3af49afc-b940-408d-a97b-6768305b4ffd · outbound

This paper cites OWSM v3.1: Bet- ter and faster open Whisper-style speech models based on E- Branchformer,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC OWSM v3.1: Bet- ter and faster open Whisper-style speech models based on E- Branchformer,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:06.615191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8af116c1-9646-47a6-809b-b98504f385a6 · outbound

This paper cites OWSM-CTC: An open encoder-only speech foundation model for speech recog- nition, translation, and language identification,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC OWSM-CTC: An open encoder-only speech foundation model for speech recog- nition, translation, and language identification,

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20098328-91a5-4062-b210-3afd133a2480 · outbound

This paper cites ML-SUPERB 2.0: Benchmarking multilingual speech models across modeling constraints, languages, and datasets,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC ML-SUPERB 2.0: Benchmarking multilingual speech models across modeling constraints, languages, and datasets,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:06.370250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f0898c31-385c-4498-bfa4-6de0d0bb9164 · outbound

This paper cites ML-SUPERB: Multilingual speech universal performance benchmark,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC ML-SUPERB: Multilingual speech universal performance benchmark,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:06.183730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ec16efb9-a8af-4f88-bb29-9e73fe4ef407 · outbound

This paper cites Con- nectionist temporal classification: labelling unsegmented se- quence data with recurrent neural networks,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Con- nectionist temporal classification: labelling unsegmented se- quence data with recurrent neural networks,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:00.544624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c36dbbf-7648-41a2-9cf4-576b0714e0f0 · outbound

This paper cites Deja-vu: Dou- ble feature presentation and iterated loss in deep transformer net- works,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Deja-vu: Dou- ble feature presentation and iterated loss in deep transformer net- works,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:06.044217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e0d7632b-470c-4b4c-8371-b744a690b0a3 · outbound

This paper cites Intermediate loss regularization for CTC-based speech recognition,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Intermediate loss regularization for CTC-based speech recognition,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:00.723893Z

Source-reported events for the cited work

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Observation 6983251b-00fe-4df6-93dd-6fbcb7f0449b · outbound

This paper cites Relaxing the conditional indepen- dence assumption of CTC-based ASR by conditioning on inter- mediate predictions,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Relaxing the conditional indepen- dence assumption of CTC-based ASR by conditioning on inter- mediate predictions,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:05.840181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 01ee8c5f-44c3-4526-be76-ae181b5fcad5 · outbound

This paper cites Improving massively mul- tilingual ASR with auxiliary CTC objectives,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Improving massively mul- tilingual ASR with auxiliary CTC objectives,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:05.642959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 312bcf2a-26c0-4844-ae19-c546fa4aa83a · outbound

This paper cites Massively multilin- gual ASR: A lifelong learning solution,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Massively multilin- gual ASR: A lifelong learning solution,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:05.428140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b96b0fd0-aeee-459a-95b7-2b686d4b84d0 · outbound

This paper cites Making more of little data: Improving low-resource automatic speech recognition using data augmentation,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Making more of little data: Improving low-resource automatic speech recognition using data augmentation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:05.218589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 52d4f11f-78ba-4d8d-b36d-b678ee26d99d · outbound

This paper cites Audio augmentation for speech recognition,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Audio augmentation for speech recognition,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:01.249951Z

Source-reported events for the cited work

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Observation 3e7ab5ea-4740-4035-a8c9-3cedf51c85ad · outbound

This paper cites Specaugment: A simple data augmentation method for automatic speech recog- nition,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Specaugment: A simple data augmentation method for automatic speech recog- nition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:05.041123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 907d4fe1-70e6-4879-8cdb-778ddd2b7206 · outbound

This paper cites SSHR: Leveraging self-supervised hierarchical representations for multilingual auto- matic speech recognition,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC SSHR: Leveraging self-supervised hierarchical representations for multilingual auto- matic speech recognition,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:04.862029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aa479f67-1f39-4185-a55c-200805a4e490 · outbound

This paper cites Attention is all you need,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Attention is all you need,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:04.717838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ebf94a9d-904d-4253-8843-9d5adcc6fcb2 · outbound

This paper cites E-Branchformer: Branch- former with enhanced merging for speech recognition,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC E-Branchformer: Branch- former with enhanced merging for speech recognition,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:04.564568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 01b3ce71-6a11-4670-90b8-04e2073782b5 · outbound

This paper cites Deep contextualized word representations,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Deep contextualized word representations,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:04.398185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 416a2466-ce4e-4bd3-b00e-17f84c281645 · outbound

This paper cites SU- PERB: Speech processing universal performance benchmark,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC SU- PERB: Speech processing universal performance benchmark,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:04.215038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a0595366-76e3-439d-9a6d-9eb4b717a307 · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC LoRA: Low-rank adaptation of large language models,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:04.024037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 76021507-1f96-4ef7-9c54-96c9da3ec33b · outbound

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

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Common voice: A massively-multilingual speech corpus,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:03.823340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e5ea9c45-a76f-41a3-8476-45db10f633f7 · outbound

This paper cites XLS-R: Self- supervised cross-lingual speech representation learning at scale,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC XLS-R: Self- supervised cross-lingual speech representation learning at scale,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:03.591150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 885cbc31-c8fc-44e8-af10-7bd49b82bcf0 · outbound

This paper cites OWSM v4: Improving open Whisper-style speech models via data scaling and cleaning,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC OWSM v4: Improving open Whisper-style speech models via data scaling and cleaning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:03.405754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 36c75989-0168-4a8b-bb9c-e17994d917cc · outbound

This paper cites ESPnet: End-to-end speech processing toolkit,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC ESPnet: End-to-end speech processing toolkit,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:03.209319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 18c9a414-c350-4883-b1d3-f34f13c566ec · outbound

This paper cites Adam: A method for stochastic optimization,.

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC Adam: A method for stochastic optimization,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:35:03.041939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 944ae7c7-580d-44d4-9f5b-a430229bb551 · inbound