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Paper Citation Record · LEDGER

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment

As of 9 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2502.05356.

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

pith.paper-citation-record.v1
2502.05356 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:43:33.402366Z

measured 45 of 45 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T11:33:32.067771Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T12:29:51.697896Z

Reference resolution

43 of 43 outbound references displayed

  • verified exact0
  • verified fuzzy39
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 984e3842-edb0-4272-82d3-118672bdee01 · outbound

This paper cites Non-intrusive speech quality assessment using neural networks,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Non-intrusive speech quality assessment using neural networks,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.625552Z

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.

source=pdf_text observed=2026-08-08T19:43:32.945007Z digest=sha256:555551a07cb51a0e0201e6588477fc0d5ab044bf071adb08e51a48461af1b6fc

Observation d3832161-2aeb-46d5-a872-6338f6daa252 · outbound

This paper cites Dnsmos: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Dnsmos: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.599675Z

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.

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Observation 5c4e95bd-f9b6-4de6-9087-0a79ec7c8005 · outbound

This paper cites NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.588782Z

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.

source=pdf_text observed=2026-08-08T19:43:32.952622Z digest=sha256:b8de97f065646e895639df08625f0a1947565a03c58df593be79ce9588d40660

Observation 0f094d11-75ad-4e40-8e8e-6197dc4d0822 · outbound

This paper cites Utilizing Self-Supervised Representations for MOS Prediction,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Utilizing Self-Supervised Representations for MOS Prediction,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.578285Z

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.

source=pdf_text observed=2026-08-08T19:43:32.956206Z digest=sha256:9589735d7ea4fac13652dc55e6ff45ff7ecafa78ed139ecaa9096ccb7211c0af

Observation f7779210-3141-43ff-8855-92ce852ea0be · outbound

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

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.568390Z

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.

source=pdf_text observed=2026-08-08T19:43:32.982620Z digest=sha256:fc6e1bfed55df3ec10df4c1abca8c93bba48a750609a25a5eb237cc04c0497a2

Observation 14c25693-df06-4e66-b87e-2b040c79595c · outbound

This paper cites Deep learning-based non-intrusive multi-objective speech assessment model with cross-domain features,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Deep learning-based non-intrusive multi-objective speech assessment model with cross-domain features,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.559127Z

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.

source=pdf_text observed=2026-08-08T19:43:33.050052Z digest=sha256:8fadaf93b9161c921e37074be4e56e7d35f4f7123f892e3065e8e58edeb24f9e

Observation 7619af06-f29b-41b0-8ed8-78acecee589e · outbound

This paper cites Hubert: Self-supervised speech representation learning by masked prediction of hidden units,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Hubert: Self-supervised speech representation learning by masked prediction of hidden units,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.549852Z

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.

source=pdf_text observed=2026-08-08T19:43:33.087716Z digest=sha256:16a3d3c37cc54bb2cdceabecd4db86302dd430da140b96dcfae6957bcae1357f

Observation 63a7ff39-63a5-4f67-8ae3-06caff32f95d · outbound

This paper cites UTMOS: UTokyo-SaruLab System for V oiceMOS Challenge 2022,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment UTMOS: UTokyo-SaruLab System for V oiceMOS Challenge 2022,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.541485Z

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.

source=pdf_text observed=2026-08-08T19:43:33.141039Z digest=sha256:82bb408a665a8918de3a5e08f95e5fec1e2240448f277b47f557cbfa0251d4da

Observation 5d99567d-bf44-4490-b1c6-f0e8b74a84d5 · outbound

This paper cites The voicemos challenge 2022,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment The voicemos challenge 2022,

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.530913Z

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.

source=pdf_text observed=2026-08-08T19:43:33.185610Z digest=sha256:022462457f7f7d182cb1b0ff9eafbe2036f5af88fd54cf826855f1d0fa3eaade

Observation 20978d2d-69bd-48c5-b477-b38f880bad41 · outbound

This paper cites Analysis of XLS-R for speech quality assessment,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Analysis of XLS-R for speech quality assessment,

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.513469Z

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.

source=pdf_text observed=2026-08-08T19:43:33.202988Z digest=sha256:c4dde61617cf601d55274550182da5a12f26fe264f047f4bdd506bb059811b7c

Observation bf27408f-01a9-4493-abdb-addd6e014cf3 · outbound

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

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment XLS-R: Self-supervised Cross-lingual Speech Rep- resentation Learning at Scale,

Reference 11

Resolution
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raw_fallback, observed 2026-08-08T19:43:34.401397Z

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.

source=pdf_text observed=2026-08-08T19:43:33.216637Z digest=sha256:69d96dcd7542345fe37c4faab2e5cde763f7684471fe9337ab6f7564d3b66871

Observation e33b6530-f4d4-48d5-9da7-55156d2218db · outbound

This paper cites ConferencingSpeech 2022 Challenge: Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge for Online Conferencing Applications,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment ConferencingSpeech 2022 Challenge: Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge for Online Conferencing Applications,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.286250Z

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.

source=pdf_text observed=2026-08-08T19:43:33.219569Z digest=sha256:b4f781c7bdc45afc280b81483fa45be9e2db1024a9f67497e921919995403d79

Observation c8cbb901-8e7a-4887-b53e-fc9d2c10c66e · outbound

This paper cites PAM: Prompting Audio-Language Models for Audio Quality Assessment.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment PAM: Prompting Audio-Language Models for Audio Quality Assessment

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-08T19:43:33.223187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:43:33.223187Z digest=sha256:3ba4b89ffff29361c8eb47fd176740410a2eaa6789bde45dbea8f2c6a28e3134

Observation 625d43d1-7018-46c1-94ec-9d13b3e33e26 · outbound

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

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment CLAP: Learning audio concepts from natural language supervision,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.202815Z

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.

source=pdf_text observed=2026-08-08T19:43:33.227000Z digest=sha256:d0dfdc6ffdf771f1a42334a2030296f0a05e8891da57f829264043a8964671e4

Observation dcc58df8-02c6-4643-9ba8-911c716d3113 · outbound

This paper cites DNN No-Reference PSTN Speech Quality Prediction,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment DNN No-Reference PSTN Speech Quality Prediction,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.154843Z

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.

source=pdf_text observed=2026-08-08T19:43:33.230521Z digest=sha256:9fb78f5f50bfc25358853dc99600e4e0c3d17a38edef5077370fd8ebd21e2fb4

Observation e8d058c6-b67f-4793-92c4-8b6fb8d7b840 · outbound

This paper cites ICASSP 2021 deep noise suppression challenge,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment ICASSP 2021 deep noise suppression challenge,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.145580Z

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.

source=pdf_text observed=2026-08-08T19:43:33.233603Z digest=sha256:529d540e0fa30a424a314f0729cfe17b508b4857141ddddc7af03c2497c83aa8

Observation 2da41cfa-4079-45ca-aad0-28cbe904e645 · outbound

This paper cites Interspeech 2022 audio deep packet loss concealment challenge,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Interspeech 2022 audio deep packet loss concealment challenge,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.135928Z

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.

source=pdf_text observed=2026-08-08T19:43:33.236428Z digest=sha256:b36ef9a749122127896e35615d006798345ec422c02d29b2609217c808e555ab

Observation 6bda20b3-854b-4514-b36b-8835862f1af4 · outbound

This paper cites ICASSP 2023 speech signal improvement challenge,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment ICASSP 2023 speech signal improvement challenge,

Reference 18

Resolution
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raw_fallback, observed 2026-08-08T19:43:34.126494Z

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.

source=pdf_text observed=2026-08-08T19:43:33.239815Z digest=sha256:12f0f51e86b5a72c6e40242884b1525b97387404a96739b46ea481baa745e109

Observation a9966b67-7a3f-41b0-bcdb-124256c2a439 · outbound

This paper cites Protocol for the collection of databases of recordings for forensic-voice-comparison research and practice,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Protocol for the collection of databases of recordings for forensic-voice-comparison research and practice,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.116837Z

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.

source=pdf_text observed=2026-08-08T19:43:33.242868Z digest=sha256:e672b562f47adc017940c70bf639895569d4b1c5d57a206b431cd2d4008207c3

Observation 81640dbc-96f6-4c3b-a9a5-c9ed7713d649 · outbound

This paper cites Tcd-voip, a research database of degraded speech for assessing quality in voip applications,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Tcd-voip, a research database of degraded speech for assessing quality in voip applications,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.107326Z

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.

source=pdf_text observed=2026-08-08T19:43:33.246003Z digest=sha256:91d7aaf9f08b6bac6f7a7dcd64a140319861463c29cb6d2e4a77ad6cea507d04

Observation b1486a6a-3722-4bf0-8f84-df10f2be82a0 · outbound

This paper cites Speech quality factors for traditional and neural- based low bit rate vocoders,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Speech quality factors for traditional and neural- based low bit rate vocoders,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.096279Z

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.

source=pdf_text observed=2026-08-08T19:43:33.249163Z digest=sha256:9822ac8b0cb5c5bf18632287c58d1c7a9121b128b5445afd19519bc2863cb3b8

Observation 00d2e5ab-5a58-4f50-af6f-7dbf0ae3bb00 · outbound

This paper cites The blizzard challenge 2023,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment The blizzard challenge 2023,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.086095Z

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.

source=pdf_text observed=2026-08-08T19:43:33.251940Z digest=sha256:b3eb4772befe285ad9698994490f796c63e79de8a4da1726c5f40c2413eb1a51

Observation 06b20e7d-1588-4fe5-a25a-d89bc6c28c25 · outbound

This paper cites Interspeech 2021 deep noise suppression challenge,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Interspeech 2021 deep noise suppression challenge,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.076199Z

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.

source=pdf_text observed=2026-08-08T19:43:33.254776Z digest=sha256:10d637f741c73848607e07c226228eb6a7007975ecf9f71a65e7268509e2eb91

Observation 0fd99a06-ef19-4837-94f4-090528df4d57 · outbound

This paper cites The ICASSP 2024 audio deep packet loss concealment grand challenge,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment The ICASSP 2024 audio deep packet loss concealment grand challenge,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.065346Z

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.

source=pdf_text observed=2026-08-08T19:43:33.257649Z digest=sha256:4369b91130b0e59abfef8e617d14263fbb48c7f46ed7d33c2d416ac5404e8221

Observation 3d204079-9e06-407c-9e57-e1f0342edff2 · outbound

This paper cites ICASSP 2024 speech signal improvement challenge,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment ICASSP 2024 speech signal improvement challenge,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:34.038989Z

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.

source=pdf_text observed=2026-08-08T19:43:33.261386Z digest=sha256:f48c7570f6583fc893187016f767a9692c45f088fa357f2baf94dac3afe067e1

Observation 234308cb-9dae-4e06-a372-aa5c783dbbd8 · outbound

This paper cites Decoupled weight decay regularization,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Decoupled weight decay regularization,

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-08T19:43:33.264663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:43:33.264663Z digest=sha256:e6f681cefda948b261d2b8381bfe6d03460656f87c63512cb712c01bfcf39845

Observation cf348730-94ae-41ab-ab80-81cd2b094213 · outbound

This paper cites Bias-aware loss for training image and speech quality prediction models from multiple datasets,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Bias-aware loss for training image and speech quality prediction models from multiple datasets,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.950483Z

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.

source=pdf_text observed=2026-08-08T19:43:33.268022Z digest=sha256:560ff7485eac250494b11fe00f01bff3ed70803ca3f0e47a77a7fef32df10b36

Observation 4846985e-5716-4d04-8b0f-11fdc87a4d49 · outbound

This paper cites Coqui TTS,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Coqui TTS,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.859732Z

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.

source=pdf_text observed=2026-08-08T19:43:33.271085Z digest=sha256:509daf0563e0531453b9a3b70887481bec2d5885f2a9fec5807936c0d570f171

Observation efadacc3-f5b8-4f51-94d8-952cf732360f · outbound

This paper cites MultiSubs: A large-scale multimodal and multilingual dataset,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment MultiSubs: A large-scale multimodal and multilingual dataset,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.763937Z

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.

source=pdf_text observed=2026-08-08T19:43:33.274545Z digest=sha256:ac7c307bf64d694b3d1648a6b356a8b2f09ec884c615fcb53434b0594d493cfe

Observation b7a8985a-286c-40ea-803e-9e5b5366ef8a · outbound

This paper cites ICASSP 2022 deep noise suppression challenge,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment ICASSP 2022 deep noise suppression challenge,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.745970Z

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.

source=pdf_text observed=2026-08-08T19:43:33.277210Z digest=sha256:77cb55ae2c96849ffc515db740e84386e4cbb5ade4dfc055416335ab8dc5dd98

Observation 0eadc82b-c415-4650-93e8-bca1b9751427 · outbound

This paper cites Deepfilternet: Perceptually motivated real-time speech enhancement,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Deepfilternet: Perceptually motivated real-time speech enhancement,

Reference 31

Resolution
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raw_fallback, observed 2026-08-08T19:43:33.736209Z

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.

source=pdf_text observed=2026-08-08T19:43:33.279982Z digest=sha256:75c328b725d584d69b6f90fff05417abaaf3d0aac2047a14e3bdce143c07c01e

Observation d0d02e1a-ae05-4169-b9ee-5f50beda2167 · outbound

This paper cites Data augmentation and loss normalization for deep noise suppression,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Data augmentation and loss normalization for deep noise suppression,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.726579Z

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.

source=pdf_text observed=2026-08-08T19:43:33.283232Z digest=sha256:9643a0b9621e595bd670c6543bc0c4366c4d0d15f45e26acf7592f1ecd22a14e

Observation 10ce886a-1ba2-4ec9-ac84-1d8e16216347 · outbound

This paper cites Real time speech enhancement in the waveform domain,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Real time speech enhancement in the waveform domain,

Reference 33

Resolution
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raw_fallback, observed 2026-08-08T19:43:33.717232Z

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.

source=pdf_text observed=2026-08-08T19:43:33.286165Z digest=sha256:5a8d204ac0578e5cfe604bd84cbcbc533af30c2a3aee5199b62428a344e59448

Observation 97bd0b74-b55b-4e08-9e7f-0b874f91e24f · outbound

This paper cites timsainb/noisereduce: v1.0,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment timsainb/noisereduce: v1.0,

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-08T19:43:33.289576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:43:33.289576Z digest=sha256:8edff8a3b3049bbf79113737156103647c083117e7c2d7bf5bd5fd5a3ffca47b

Observation beec576b-396a-49e3-bc9c-3e524f345f78 · outbound

This paper cites Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.707451Z

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.

source=pdf_text observed=2026-08-08T19:43:33.293286Z digest=sha256:95f7be6728d80fc5220d63c1b3c2cf6a7f7ff4a0c6aa2d88803bd17a1b57cf81

Observation 208fe748-ba1e-4ed7-8b23-4982f3369f12 · outbound

This paper cites LPCNET: Improving neural speech synthesis through linear prediction,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment LPCNET: Improving neural speech synthesis through linear prediction,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.697366Z

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.

source=pdf_text observed=2026-08-08T19:43:33.296233Z digest=sha256:f3df855ed7322d60fb0ec122cc8bbeff0500adeb0fb83d5fa16ed042696f069d

Observation 230cf298-d446-49a7-968a-199771bc258d · outbound

This paper cites High-quality, low-delay music coding in the Opus codec,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment High-quality, low-delay music coding in the Opus codec,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.686790Z

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.

source=pdf_text observed=2026-08-08T19:43:33.299835Z digest=sha256:990907ec080ea46d1097ea09e42ed3e1646cbc1d48324e73c0cf1de332b01fe4

Observation 8f312ca1-d929-4a04-aeb6-0f6f525efa4a · outbound

This paper cites High fidelity neural audio compression,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment High fidelity neural audio compression,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.676166Z

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.

source=pdf_text observed=2026-08-08T19:43:33.302695Z digest=sha256:28415d72841be3f750d27ec9de0291010999530104ba03ad0fff8b8255245edb

Observation f520b62a-9348-4fd5-b366-9a68ded2e3e6 · outbound

This paper cites Effect of noise suppression losses on speech distortion and asr performance,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Effect of noise suppression losses on speech distortion and asr performance,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.665340Z

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.

source=pdf_text observed=2026-08-08T19:43:33.305921Z digest=sha256:2dbf57aab66517bc10379dcba8b91c6b277d206af380bf3cf49d9778ac0e818d

Observation 69b71717-5b64-4c06-8161-7d2eca6ca5a8 · outbound

This paper cites Importance estimation for neural network pruning,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Importance estimation for neural network pruning,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.655032Z

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.

source=pdf_text observed=2026-08-08T19:43:33.308647Z digest=sha256:176aa5a1be30f2074ca6852dc0a70055ff5f52bca745bab2eaa4c172d1d1d2ba

Observation 5901f9fc-c5d5-4420-be81-546260fd3e2d · outbound

This paper cites Dnsmos p.835: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Dnsmos p.835: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.621328Z

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.

source=pdf_text observed=2026-08-08T19:43:33.315733Z digest=sha256:fb4259b1905ddc9f7b87407d4dcccb99d4373c1b979a61d1be9603ce5bdca446

Observation eaac26f2-2a17-4590-8bf6-ae8e28ee66c3 · outbound

This paper cites Torchaudio-squim: Reference-less speech quality and intelligibility measures in torchaudio,.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment Torchaudio-squim: Reference-less speech quality and intelligibility measures in torchaudio,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T19:43:33.538530Z

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.

source=pdf_text observed=2026-08-08T19:43:33.368413Z digest=sha256:2fba6ba9b87347f0f475b3d8bc3d253e4aa8ec693d14d9bee890244fe204bd79

Observation b8b55980-8ed0-4bd2-9495-955ee0c8f340 · outbound

This paper cites The State of Sparsity in Deep Neural Networks.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment The State of Sparsity in Deep Neural Networks

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-08T19:43:33.402366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:43:33.402366Z digest=sha256:4eefef88d31b0d02c07c708cc8b06d9d7d2076adfa73a4140b28a14fd2b5ee9e

Pith citing papers

Observation 2ec7a0cc-dba3-45be-8661-d969736993b5 · inbound

ISCSLP 2026 CoT-TTS Challenge: Chain-of-Thought Reasoning for Context-Aware Text-to-Speech cites this paper.

ISCSLP 2026 CoT-TTS Challenge: Chain-of-Thought Reasoning for Context-Aware Text-to-Speech Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-07-04T08:29:42.169028Z

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.

source=pdf_text observed=2026-06-26T11:33:32.067771Z digest=sha256:c974e5fcbda65c17d9fa6319a8f3f0d71a48cabfd781a1ed70199b5a9334f984

Observation fb71144c-0b83-4897-9564-67f4e1a4a8ab · inbound

Don't Listen to Me: A Lightweight, Low-Latency Model for Own-Voice Cancellation in Far-Field Speech Enhancement cites this paper.

Don't Listen to Me: A Lightweight, Low-Latency Model for Own-Voice Cancellation in Far-Field Speech Enhancement Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-07-04T12:29:51.699304Z

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.

source=pdf_text observed=2026-06-26T06:49:46.425849Z digest=sha256:7e99904f58f3b77ca808a8702b7aa78231df035bd8dd36110328c23a3b5c891d