Paper Citation Record · LEDGER

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data

As of 9 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2606.13507.

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

pith.paper-citation-record.v1
2606.13507 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T07:00:53.430064Z

measured 39 of 39 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 2026-06-27T07:00:53.430064Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T14:38:28.995597Z

Reference resolution

38 of 38 outbound references displayed

verified exact5
verified fuzzy0
unresolved31
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e695839d-4feb-47c5-928f-9cadca1dab27 · outbound

This paper cites Despite recent progress, S2ST remains strongly constrained by the quality of available training data.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Despite recent progress, S2ST remains strongly constrained by the quality of available training data

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c67dfb84-db56-4ee6-a4d8-742163b3515b · outbound

This paper cites The re- sulting audio-language model can directly assess paired speech by jointly considering acoustic fidelity and cross-lingual seman- tic consistency.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data The re- sulting audio-language model can directly assess paired speech by jointly considering acoustic fidelity and cross-lingual seman- tic consistency

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdbc7229-4dbb-48b3-bcd7-c2b2a4e51d40 · outbound

This paper cites Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-03T14:38:28.996792Z

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 689a222e-8f53-4440-944f-779f459ca227 · outbound

This paper cites an unresolved cited work.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f796ab81-5dcd-4e3c-a67b-1edca919846c · outbound

This paper cites Comparison with Baseline Table 1 summarizes the comparison between filtering strategies on CVSS-C + SpeechMatrix data.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Comparison with Baseline Table 1 summarizes the comparison between filtering strategies on CVSS-C + SpeechMatrix data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab7aefc4-d6d5-45f8-b8a6-14f1175a4e4a · outbound

This paper cites an unresolved cited work.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 179e8a0b-8bdb-4ebc-88d7-280815b5bd6d · outbound

This paper cites an unresolved cited work.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a074fdd-8412-40bc-b4c8-b1e915148d16 · outbound

This paper cites The au- thors reviewed and edited all AI-assisted outputs and take full responsibility for the content of the paper.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data The au- thors reviewed and edited all AI-assisted outputs and take full responsibility for the content of the paper

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdf80692-60a1-4d23-b221-12a9f3bcdd68 · outbound

This paper cites Sequence-to-sequence models can directly translate foreign speech,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Sequence-to-sequence models can directly translate foreign speech,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 561245f1-be85-4d62-9d85-be84139700d5 · outbound

This paper cites Direct speech-to-speech translation with a sequence- to-sequence model,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Direct speech-to-speech translation with a sequence- to-sequence model,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35f77e4b-c076-4ead-94ab-6374768b798a · outbound

This paper cites Denoising neural machine translation training with trusted data and online data selection,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Denoising neural machine translation training with trusted data and online data selection,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47b92830-8466-4e39-8682-fe9223f2e208 · outbound

This paper cites Available: https://aclanthology.org/W18-6314/.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Available: https://aclanthology.org/W18-6314/

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56dca6b7-648b-4b57-8229-372d53deeef5 · outbound

This paper cites Curriculum learning for domain adaptation in neural machine translation,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Curriculum learning for domain adaptation in neural machine translation,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50666a76-21d6-4c12-bdf0-016d9df6974c · outbound

This paper cites Low-resource corpus filtering using multilingual sentence embeddings,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Low-resource corpus filtering using multilingual sentence embeddings,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0cad8fc-2519-4888-8863-314b3790aff5 · outbound

This paper cites Effective parallel corpus mining using bilingual sentence embeddings,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Effective parallel corpus mining using bilingual sentence embeddings,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61f637bd-434f-4940-a114-1ac737bc6fc5 · outbound

This paper cites A Case Study on Filtering for End-to-End Speech Translation.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data A Case Study on Filtering for End-to-End Speech Translation

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:38:28.988926Z

Source-reported events for the cited work

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Observation 9ffc81a9-e5d3-4cb5-b64f-d3997d296f39 · outbound

This paper cites BLASER 2.0: a metric for evaluation and quality estimation of massively multilingual speech and text translation,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data BLASER 2.0: a metric for evaluation and quality estimation of massively multilingual speech and text translation,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f86512c1-3f96-4a27-9de6-e4bfbee3c51a · outbound

This paper cites Large language models are state-of-the-art evaluators of translation quality,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Large language models are state-of-the-art evaluators of translation quality,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89523885-5463-41cf-a5f6-ecc97e26456e · outbound

This paper cites Multilingual data filtering using synthetic data from large language models,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Multilingual data filtering using synthetic data from large language models,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7389f91e-8317-4a48-a01c-68ba33572748 · outbound

This paper cites Audio large language models can be descriptive speech quality evaluators,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Audio large language models can be descriptive speech quality evaluators,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c8cc6cd-a618-4569-aef5-029501514883 · outbound

This paper cites Audio Large Language Models Can Be Descriptive Speech Quality Evaluators.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Audio Large Language Models Can Be Descriptive Speech Quality Evaluators

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:38:28.985717Z

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 fce1016f-3e47-4f02-997c-5bf5a17e1007 · outbound

This paper cites Self-training with noisy student improves imagenet classification,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Self-training with noisy student improves imagenet classification,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b937b42f-9709-43b1-9502-df827bac0640 · outbound

This paper cites Pseudo Label Is Better Than Human Label,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Pseudo Label Is Better Than Human Label,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3faa27a0-6421-4394-85a1-223aff10bb14 · outbound

This paper cites Measuring speech qual- ity for text-to-speech systems: Development and assessment of a modified mean opinion score (mos) scale,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Measuring speech qual- ity for text-to-speech systems: Development and assessment of a modified mean opinion score (mos) scale,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21b01541-59f2-4857-9898-d5f6222169ee · outbound

This paper cites Direct speech-to-speech translation with discrete units,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Direct speech-to-speech translation with discrete units,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6d07884-2602-4af8-ac11-73045ee5cf2e · outbound

This paper cites Transpeech: Speech-to-speech translation with bilateral pertur- bation,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Transpeech: Speech-to-speech translation with bilateral pertur- bation,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f589b73-4369-403b-81dc-477e46f0f780 · outbound

This paper cites CVSS corpus and massively multilingual speech-to-speech translation,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data CVSS corpus and massively multilingual speech-to-speech translation,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bac26408-769d-4f94-9ef4-ca3b501c5c97 · outbound

This paper cites Speechmatrix: A large-scale mined corpus of multilingual speech-to-speech translations,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Speechmatrix: A large-scale mined corpus of multilingual speech-to-speech translations,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

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Observation f8449b35-aa9a-4915-b082-4cc09ab19484 · outbound

This paper cites Radiometric noise characterization of the 183-664 GHz front-end receivers for the MetOp-SG Ice Cloud Imager instrument – prospects for future missions.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Radiometric noise characterization of the 183-664 GHz front-end receivers for the MetOp-SG Ice Cloud Imager instrument – prospects for future missions

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T07:10:41.652178Z

Source-reported events for the cited work

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Observation 3b9c586f-43df-4b5b-bf0a-77a135af15f3 · outbound

This paper cites UTMOS: UTokyo-SaruLab system for V oice- MOS challenge 2022,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data UTMOS: UTokyo-SaruLab system for V oice- MOS challenge 2022,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

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Observation fa0408cd-9842-4edf-9a5d-8e1f0a4a2d0c · outbound

This paper cites Qwen3 Technical Report.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Qwen3 Technical Report

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-03T14:38:28.994373Z

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 81286504-42e2-4326-bf07-3c9d2c4e59ff · outbound

This paper cites Robust Speech Recognition via Large-Scale Weak Supervision.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Robust Speech Recognition via Large-Scale Weak Supervision

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-07-03T14:38:28.991386Z

Source-reported events for the cited work

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Observation af421d1a-3b01-49c0-91db-d1676743c830 · outbound

This paper cites LLaMAX: Scaling linguistic horizons of LLM by enhancing translation capabilities beyond 100 languages,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data LLaMAX: Scaling linguistic horizons of LLM by enhancing translation capabilities beyond 100 languages,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

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Observation 4c154d9b-3c63-41d7-aa3c-88f3b8aa682c · outbound

This paper cites Bleurt: Learning robust metrics for text generation,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Bleurt: Learning robust metrics for text generation,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

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Observation 649dd91b-ebc1-4529-ac90-091a6da42d0f · outbound

This paper cites From ranknet to lambdarank to lambdamart: An overview,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data From ranknet to lambdarank to lambdamart: An overview,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

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Unavailable: canonical work link unavailable.

Observation aea19f7b-4eb8-4dce-819e-32a08c149eb0 · outbound

This paper cites Lightgbm: A highly efficient gradient boosting deci- sion tree,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Lightgbm: A highly efficient gradient boosting deci- sion tree,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

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Observation cb40194e-bc88-46be-96a3-2354bbb3c89d · outbound

This paper cites Qwen2-Audio Technical Report.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Qwen2-Audio Technical Report

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-07-03T14:38:28.982740Z

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 df5d4a69-599f-4294-99e9-4023c8418508 · outbound

This paper cites Audio flamingo: A novel audio language model with few-shot learning and dialogue abilities,.

Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data Audio flamingo: A novel audio language model with few-shot learning and dialogue abilities,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-27T07:00:53.430064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation fdbc7229-4dbb-48b3-bcd7-c2b2a4e51d40 · inbound