Paper Citation Record · LEDGER

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio

As of 22 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2506.23582.

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

pith.paper-citation-record.v1
2506.23582 v1

Coverage vector

measured 39 of 39 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T21:41:24.207276Z

measured 40 of 40 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.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:41:21.333361Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:41:24.430826Z

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation f56a6ed1-d90d-4f35-9f31-859ccba6066f · outbound

This paper cites a dog barking behind a human speech,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio a dog barking behind a human speech,

Reference 1

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This paper cites RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio

Reference 2

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Observation 5bceae05-baab-4c89-aa9f-cbc2d88d8296 · outbound

This paper cites Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound

Reference 3

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This paper cites 2” or higher, we added the exclusion of listeners with an average original audio rating of “6.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio 2” or higher, we added the exclusion of listeners with an average original audio rating of “6

Reference 4

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Observation bca3e593-4e97-4146-829b-69f2ed23b05f · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 5

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RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Unresolved cited work

Reference 6

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RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Unresolved cited work

Reference 7

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Observation b1d044a9-a83d-4535-aae2-d532992e074b · outbound

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

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio UTMOS: UTokyo-SaruLab system for V oice- MOS Challenge 2022,

Reference 8

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This paper cites Diffsound: Discrete diffusion model for text-to-sound genera- tion,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Diffsound: Discrete diffusion model for text-to-sound genera- tion,

Reference 9

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Observation 0c15c510-645e-4f29-a594-5ed720865bd9 · outbound

This paper cites Sound synthesis for impact sounds in video games,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Sound synthesis for impact sounds in video games,

Reference 10

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Observation 230d1527-c23a-4766-8c98-9676f450937b · outbound

This paper cites Challenge on sound scene synthe- sis: Evaluating text-to-audio generation,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Challenge on sound scene synthe- sis: Evaluating text-to-audio generation,

Reference 11

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Observation 9d038414-0e20-426e-a83e-519bc03d6f4f · outbound

This paper cites The V oiceMOS Challenge 2024: Beyond speech quality prediction,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio The V oiceMOS Challenge 2024: Beyond speech quality prediction,

Reference 12

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Observation 575f9da5-92f0-4e6c-8278-bf9af17f6c91 · outbound

This paper cites Subjective-aligned dataset and metric for text-to-video quality assessment,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Subjective-aligned dataset and metric for text-to-video quality assessment,

Reference 13

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Observation 105ac452-f0d2-4310-a374-341099b3a6e2 · outbound

This paper cites Environmental sound synthesis from vocal imitations and sound event labels,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Environmental sound synthesis from vocal imitations and sound event labels,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:41:28.698899Z

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Observation 9ccadc8a-8fb6-4579-992f-806bef2474af · outbound

This paper cites Make-an-audio: Text-to-audio gen- eration with prompt-enhanced diffusion models,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Make-an-audio: Text-to-audio gen- eration with prompt-enhanced diffusion models,

Reference 15

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Observation b0ebc53f-c036-4544-a474-bcd78621ca1b · outbound

This paper cites Text-to- audio generation using instruction guided latent diffusion model,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Text-to- audio generation using instruction guided latent diffusion model,

Reference 16

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Observation 12b470e2-c5a9-43ce-904c-3825d3b472df · outbound

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

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,

Reference 17

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Observation 7203e59d-5374-4215-b076-8c93d4ef1fb5 · outbound

This paper cites WavLM: Large-scale self-supervised pre-training for full stack speech processing,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio WavLM: Large-scale self-supervised pre-training for full stack speech processing,

Reference 18

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This paper cites Animal” category shows both of statistically significant differ- ences and interaction. Figure 2(a). shows that synthesized audio in the “Animal.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Animal” category shows both of statistically significant differ- ences and interaction. Figure 2(a). shows that synthesized audio in the “Animal

Reference 19

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Observation 6beaa23e-2537-40ab-bcc8-1f182c8af643 · outbound

This paper cites PAM: Prompting audio-language models for audio quality assessment,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio PAM: Prompting audio-language models for audio quality assessment,

Reference 20

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Observation 818e93a2-30ca-4461-b3a1-625b0cfab09d · outbound

This paper cites Audio-text mod- els do not yet leverage natural language,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Audio-text mod- els do not yet leverage natural language,

Reference 21

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Observation 1f895f04-a02e-432f-bfb2-63baf86708a3 · outbound

This paper cites AudioCaps: Generat- ing captions for audios in the wild,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio AudioCaps: Generat- ing captions for audios in the wild,

Reference 22

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Observation 7dfe310c-937c-4c4f-a7fa-5adb96f9ac70 · outbound

This paper cites AudioLDM: text-to-audio generation with latent diffusion models,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio AudioLDM: text-to-audio generation with latent diffusion models,

Reference 23

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Observation df6faa69-4fec-4869-bc10-86d9f8f9d286 · outbound

This paper cites AudioLDM 2: Learn- ing holistic audio generation with self-supervised pretraining,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio AudioLDM 2: Learn- ing holistic audio generation with self-supervised pretraining,

Reference 24

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This paper cites Tango 2: Aligning diffusion-based text-to-audio generations through direct preference optimization,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Tango 2: Aligning diffusion-based text-to-audio generations through direct preference optimization,

Reference 25

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This paper cites Toward verifiable and repro- ducible human evaluation for text-to-image generation,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Toward verifiable and repro- ducible human evaluation for text-to-image generation,

Reference 26

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This paper cites On a test of whether one of two random variables is stochastically larger than the other,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio On a test of whether one of two random variables is stochastically larger than the other,

Reference 27

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This paper cites Use of ranks in one-criterion variance analysis,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Use of ranks in one-criterion variance analysis,

Reference 28

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This paper cites A multiple comparison rank sum test: treatments versus control,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio A multiple comparison rank sum test: treatments versus control,

Reference 29

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This paper cites The aligned rank transform for nonparametric factorial analyses using only anova procedures,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio The aligned rank transform for nonparametric factorial analyses using only anova procedures,

Reference 30

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This paper cites A new readability yardstick.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio A new readability yardstick

Reference 31

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RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio BYOL for Audio: Exploring pre-trained general-purpose audio representations,

Reference 32

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This paper cites LDNet: Unified listener dependent modeling in MOS prediction for syn- thetic speech,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio LDNet: Unified listener dependent modeling in MOS prediction for syn- thetic speech,

Reference 33

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RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Bidirectional LSTM networks for improved phoneme classification and recog- nition,

Reference 34

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This paper cites MBNet: MOS prediction for synthesized speech with mean-bias network,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio MBNet: MOS prediction for synthesized speech with mean-bias network,

Reference 35

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RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Class- balanced loss based on effective number of samples,

Reference 36

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This paper cites Adam: A method for stochastic op- timization,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Adam: A method for stochastic op- timization,

Reference 37

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This paper cites Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation,

Reference 38

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Observation 2115d22c-8f3f-41b3-8b74-39f0a190b9de · outbound

This paper cites CLAP: Learning audio concepts from natural language supervision,.

RELATE: Subjective evaluation dataset for automatic evaluation of relevance between text and audio CLAP: Learning audio concepts from natural language supervision,

Reference 39

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Pith citing papers

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