Pith. sign in

Paper Citation Record · LEDGER

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination

As of 11 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2606.02913.

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

pith.paper-citation-record.v1
2606.02913 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T12:23:00.921024Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T12:23:00.921024Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T01:16:24.561296Z

Reference resolution

48 of 48 outbound references displayed

  • verified exact4
  • verified fuzzy0
  • unresolved43
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c004b19d-3c82-4121-891b-ec09c9789681 · outbound

This paper cites an unresolved cited work.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Unresolved cited work

Reference 1

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:6e29e5a374eef6813c329c71c823b7764e4770a89b964edabe1e966dd380f5c4

Observation cc2861ac-2084-4112-b4ac-51364defd785 · outbound

This paper cites A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-07-02T01:16:24.562747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:a5907412db3baa04a2afd5a8ad71bf964c8ed0d7961b29a05e9751f7afee8f9d

Observation f4dde1dd-01e9-490c-ae35-33c3cc8cda31 · outbound

This paper cites an unresolved cited work.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Unresolved cited work

Reference 3

Resolution
malformed identifier
arxiv_id, observed 2026-07-02T01:16:24.565884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:d8642c1ff8a9de8529b8a67454a757acc3178f78bd327a7d09e08d793921b077

Observation 490ce62e-5b2a-4546-ad59-a58174ea1a19 · outbound

This paper cites Our analysis also shows that, for SE tasks, discriminative and GAN-based methods result in faster training times and can achieve better efficiency in terms of training data.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Our analysis also shows that, for SE tasks, discriminative and GAN-based methods result in faster training times and can achieve better efficiency in terms of training data

Reference 4

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:ccbeabf9dacdd3723816281b7a4ccde1db38381757c2bdae46d1518a04795862

Observation 1e6dab42-6753-425e-84a2-d5abe634a07c · outbound

This paper cites DCCRN: Deep complex convolution recurrent network for phase-aware speech enhancement,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination DCCRN: Deep complex convolution recurrent network for phase-aware speech enhancement,

Reference 5

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:fed08cf641769feb317813f2275b95c55220074d9a8a41162613dab9ac4cb17b

Observation 2e2f9e39-153a-42af-ad89-79c3d8444152 · outbound

This paper cites Real- time denoising and dereverberation with tiny recurrent U-Net,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Real- time denoising and dereverberation with tiny recurrent U-Net,

Reference 6

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:10d5b0626c91873a9f5a31ef18270130cf38d4e459f899bf15fa6bcd9db6ae4a

Observation 17b4471b-64ce-4ed7-9567-5a55aaf163c7 · outbound

This paper cites Ultra low complexity deep learning based noise suppression,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Ultra low complexity deep learning based noise suppression,

Reference 7

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:ddd7ec5876e10ba946635ede66351a532626b4c40d173255b201d036fbc127ce

Observation 256bc3fd-3dd6-4f60-b076-d2a905229f3f · outbound

This paper cites Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement,

Reference 8

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:b2c288461e3e7a1044747e0cb788ffd5c4c87e47a53508ccfd4ca2a58a3e13b9

Observation 0537a5b2-9b4a-459b-b2d9-d421e31e6870 · outbound

This paper cites MetricGAN+: An improved version of MetricGAN for speech enhancement,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination MetricGAN+: An improved version of MetricGAN for speech enhancement,

Reference 9

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:ae28bde7f45cea4203609f39f087111918f035ccaeca21487349f499a3a55fa8

Observation ad4ca6fd-5440-4892-8047-b98aac6a9c24 · outbound

This paper cites Generative adversarial networks in speech enhancement: A survey,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Generative adversarial networks in speech enhancement: A survey,

Reference 10

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:33536c684313dbd371970233ed513549b2a66a7ebded25a7b2e648ac92ecc1e6

Observation 72de2439-7fea-43f9-85fb-ac8951baaedb · outbound

This paper cites Speech enhancement and dereverberation with diffusion-based gener- ative models,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Speech enhancement and dereverberation with diffusion-based gener- ative models,

Reference 11

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:0252fbd0d164bb5804e8ded04134d2ea1d88461c2d51eaa4407a691e06817eef

Observation 810e5301-168f-48c2-8655-b4f089cb2a9b · outbound

This paper cites Conditional latent diffusion-based speech enhancement via dual context learning,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Conditional latent diffusion-based speech enhancement via dual context learning,

Reference 12

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:ca64be3134c32a7ec96b418d0131edf771be10f14b3932e882b9d8838273e912

Observation 5a69414e-d7d3-4d9f-af6f-e7b0fcb86aa0 · outbound

This paper cites Bridging the gap between monau- ral speech enhancement and recognition with distortion-independent acoustic modeling,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Bridging the gap between monau- ral speech enhancement and recognition with distortion-independent acoustic modeling,

Reference 13

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:5350ea939a04607d429dda4f831b1f8354cdf1974f1c3bf30415fd6b682161c3

Observation dd8ea175-3549-4d74-b761-f4c47ddfb9ba · outbound

This paper cites Leveraging discrimina- tive latent representations for conditioning gan-based speech enhance- ment,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Leveraging discrimina- tive latent representations for conditioning gan-based speech enhance- ment,

Reference 14

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:3534ea5d11ab8ab024cb25a887f842d22d0b7d9859e84d8eaf20a562f329a5ba

Observation 23ab1beb-9115-4961-9a8d-23cf6439760e · outbound

This paper cites Universal score- based speech enhancement with high content preservation,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Universal score- based speech enhancement with high content preservation,

Reference 15

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:137e6728cf1927f75d75b28b0ad71935c3781cd4c20e6f0c4e1cfff4ac40c7ed

Observation bb5df4a9-c085-4643-8d05-e948fb14093c · outbound

This paper cites Investigating the design space of diffusion models for speech enhancement,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Investigating the design space of diffusion models for speech enhancement,

Reference 16

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:a2d0db38a346aa08b5932e7b992ef4cb45eba1e2b56ebd4fe69494b49b754c5e

Observation a3c6e3fd-f1a9-47da-823c-d4658c6c9ced · outbound

This paper cites GAN-based speech enhancement for low SNR using latent feature conditioning,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination GAN-based speech enhancement for low SNR using latent feature conditioning,

Reference 17

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:5658bd3e59bba4150cde5019e74fdc99b829e681b4f78baf3f08752b9f5cd0ff

Observation efb246e1-8997-4888-9651-20d5654752c4 · outbound

This paper cites In- vestigating RNN-based speech enhancement methods for noise-robust text-to-speech,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination In- vestigating RNN-based speech enhancement methods for noise-robust text-to-speech,

Reference 18

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:73f3c3001f72311140b8e47e60a904e80b6c41950a4190cef2d4b49b006e3501

Observation ac0bd3dc-22c1-4815-8200-7a63dff0a3d7 · outbound

This paper cites EARS: An anechoic fullband speech dataset benchmarked for speech enhancement and dereverberation,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination EARS: An anechoic fullband speech dataset benchmarked for speech enhancement and dereverberation,

Reference 19

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:e69071eccbea055bbf1b929baec33d77d8a154478f2d3c7f1db759e224b1ad96

Observation 9d8c6862-1138-4ca7-8251-eeadfcb1c53f · outbound

This paper cites WHAM!: Extending speech separation to noisy environments,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination WHAM!: Extending speech separation to noisy environments,

Reference 20

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:815225daf439f35e0d3fad6c3fcd4a82142a6b69ddaf09455e598b15626ebff1

Observation ff95e628-d90d-422c-93ea-73a45e910ffe · outbound

This paper cites CMGAN: Conformer-based met- ric GAN for speech enhancement,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination CMGAN: Conformer-based met- ric GAN for speech enhancement,

Reference 21

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:6b28da0d062ef1f7bd61b568efd3bfd80669b1d9dd9d2bee3eb2cfe3cdef8310

Observation 0529fc40-3cc8-4233-bdaf-27056a7811a0 · outbound

This paper cites MetricGAN: Generative adversarial networks based black-box metric scores optimization for speech enhancement,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination MetricGAN: Generative adversarial networks based black-box metric scores optimization for speech enhancement,

Reference 22

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:6d6c4044810918df656a729a58a44f8903dc295a3ad846b89af42984b3f90cf1

Observation f13960fb-5aa6-45f4-a203-47b7e334a556 · outbound

This paper cites Comparative analysis of discriminative deep learning-based noise reduction methods in low SNR scenarios,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Comparative analysis of discriminative deep learning-based noise reduction methods in low SNR scenarios,

Reference 23

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:0243f82bccfdd4b6833110bdf170f95b2386cacb06e719ae4571c178b0bd5005

Observation a8a5a1f7-986b-4c75-9167-e9239671e6b1 · outbound

This paper cites Analysing diffusion-based generative approaches versus discriminative ap- proaches for speech restoration,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Analysing diffusion-based generative approaches versus discriminative ap- proaches for speech restoration,

Reference 24

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:33f121cca2eaa81fe4f064644a36a1b6714d7729e60468190fc6ae5f4b930837

Observation 83c6be77-1238-4467-902c-a0173f64a757 · outbound

This paper cites Investigating training ob- jectives for generative speech enhancement,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Investigating training ob- jectives for generative speech enhancement,

Reference 25

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:fa546cd2294389496d204a3ee31e2ecb5de6abf63735decceeda965d9ec2286a

Observation 954c5b07-d951-4e29-a0f4-25d10080927d · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 26

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:13c1e4623de380c2060c38530075b29d6e30d23bf884a816f6ff05b24aa2e2f4

Observation 4ee284fc-3695-4f94-aca7-c081b424b9b2 · outbound

This paper cites Denoising diffusion probabilistic mod- els,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Denoising diffusion probabilistic mod- els,

Reference 27

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:9a925520c8802d9c2e4bc1f2610e4fb34e5330a73fc672b1c6cc8a8ad0cfd5e4

Observation 5cd3bb1d-62fd-4fe8-a167-4e7688dd1e5a · outbound

This paper cites Reducing the prior mismatch of stochastic differential equations for diffusion-based speech enhancement,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Reducing the prior mismatch of stochastic differential equations for diffusion-based speech enhancement,

Reference 28

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:bea877bf2db4db06bcfae8c347471ae764c1b9a25dc0e5992c5152f58360e2c0

Observation 076bb23c-7f34-42aa-aed3-646113180056 · outbound

This paper cites GALD-SE: Guided anisotropic lightweight diffusion for efficient speech enhancement,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination GALD-SE: Guided anisotropic lightweight diffusion for efficient speech enhancement,

Reference 29

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:84cfa598a722fb014418f7af9b68cd9a13664b1e8ed92d74462847d38a7fdd18

Observation 120ba071-722b-48fc-942f-0a3a8f98d138 · outbound

This paper cites FlowSE: Flow matching-based speech enhancement,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination FlowSE: Flow matching-based speech enhancement,

Reference 30

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:0156ac8976fb26cd5644dcd4a34128da57591758b271845a0dff9bd81b533040

Observation 9ca822b2-234d-4cb0-95a6-bd1db8f1b1ac · outbound

This paper cites Consistency mod- els,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Consistency mod- els,

Reference 31

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:2c466f8bc12d89307a40b27db3b1048e6f922eca5ba109226e2671bb81f3ff58

Observation d992e6a3-7a37-4817-9b42-dcd5880bb495 · outbound

This paper cites SE-Bridge: Speech Enhancement with Consistent Brownian Bridge.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination SE-Bridge: Speech Enhancement with Consistent Brownian Bridge

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-07-02T01:16:24.554651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:e2be22288f9c4ff11206e64a28f2ce75cc1f894c7e84dcca5207bddad8d91020

Observation 7852d9c5-45d2-428f-b6a2-8a9ae77c0b68 · outbound

This paper cites Score-based generative modeling through stochastic dif- ferential equations,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Score-based generative modeling through stochastic dif- ferential equations,

Reference 33

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:4a196bd20957b76d5f853bd02cfa3e22a537d884618f808da3247b1d2cdcaec4

Observation 3aa2656c-c2c8-45a8-b32b-506da945c872 · outbound

This paper cites FunCodec: A fundamental, re- producible and integrable open-source toolkit for neural speech codec,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination FunCodec: A fundamental, re- producible and integrable open-source toolkit for neural speech codec,

Reference 34

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:6040eded3f64ebac9f2dfc0253f5f785f9f6abfbd5aa3aceb8c80d2acafbb05c

Observation be89f02b-9836-4885-b023-1b78bf92fb32 · outbound

This paper cites The Inter- Speech 2020 deep noise suppression challenge: Datasets, subjective testing framework, and challenge results,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination The Inter- Speech 2020 deep noise suppression challenge: Datasets, subjective testing framework, and challenge results,

Reference 35

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:5e3023a592035679077b39fb55a21f6148daa852ce60b0110f3445cad1c4c012

Observation a0f240cb-96cc-40c7-a00c-a0d1ac639ae3 · outbound

This paper cites Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs,

Reference 36

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:8f83b1ca3124b07c6469193253ab7897a6975d27d2d65510b741735b2a67ff81

Observation 013aba9d-4320-4cfa-8b4b-69c7830e0686 · outbound

This paper cites Objective measures for predict- ing speech intelligibility in noisy conditions based on new band- importance functions,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Objective measures for predict- ing speech intelligibility in noisy conditions based on new band- importance functions,

Reference 37

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:e663da559aae454d896002d56db9a9491a0eadc15eae7f7cf535bb66cac0d5f6

Observation b261f0c9-5872-4256-8146-2907e74acf1a · outbound

This paper cites SDR–half- baked or well done?,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination SDR–half- baked or well done?,

Reference 38

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:2d274599a5776aac51b9c66281ad7199780ab97f6f50df695b32c575378e2214

Observation 04294d74-6657-4365-beee-27e5537478d7 · outbound

This paper cites DNSMOS: A non-intrusive perceptual objective speech quality metric to evaluate noise suppres- sors,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination DNSMOS: A non-intrusive perceptual objective speech quality metric to evaluate noise suppres- sors,

Reference 39

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:a34f7d3885fd3ccf40fbbef8dfd375bb55815964a4908e4a2868f47cee10f64f

Observation 72cdb7ee-3e72-43ad-915e-593bb84e44f1 · outbound

This paper cites SCOREQ: Speech quality as- sessment with contrastive regression,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination SCOREQ: Speech quality as- sessment with contrastive regression,

Reference 40

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:23daa82c6f28227ae0a66fe4de6c56423f2ec7a424549167becf2375b4dbc91a

Observation 2beb2d21-21fc-460c-bca1-bb7415ad69fa · outbound

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

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Robust speech recognition via large-scale weak super- vision,

Reference 41

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:06de863d646b97291d84044a4e607d398ae50373042b5f4e17f78af60ae77869

Observation dcb2db89-3024-4530-82d8-711642dcf0c3 · outbound

This paper cites From WER and RIL to MER and WIL: Improved evaluation measures for connected speech recognition.,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination From WER and RIL to MER and WIL: Improved evaluation measures for connected speech recognition.,

Reference 42

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:148d2e0d778538ee76a5dd43c2cde505085ace903b11c6624b76eb18c37bb9d7

Observation 0ff2e71e-4262-4522-8a81-5d5aaa13aeba · outbound

This paper cites Evaluation metrics for generative speech enhance- ment methods: Issues and perspectives,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Evaluation metrics for generative speech enhance- ment methods: Issues and perspectives,

Reference 43

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:ce8f99a55ef55180bd726b5c41639415ab2179280502219037449a4b337e5e1d

Observation 56f8a289-1e8c-4cdb-9cee-942c213e0ab6 · outbound

This paper cites DeepFilterGAN: A Full-band Real-time Speech Enhancement System with GAN-based Stochastic Regeneration.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination DeepFilterGAN: A Full-band Real-time Speech Enhancement System with GAN-based Stochastic Regeneration

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-07-02T01:16:24.560655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:6968c58bc9f686652026af10e36cfdcf87509a1b300d40cc7deb38348b4b22b6

Observation 9ff2d59c-5bff-4005-8aa6-993c474c766b · outbound

This paper cites Deep- Filternet2: Towards real-time speech enhancement on embedded de- vices for full-band audio,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Deep- Filternet2: Towards real-time speech enhancement on embedded de- vices for full-band audio,

Reference 45

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:6b1da2c7c0e2d5c85a5011749ec4eb785367c7d67f1d56159a4d7c84b4aedb5a

Observation c7898777-e3a1-47c9-b5db-e8d765c6516e · outbound

This paper cites A hybrid approach for low-complexity joint acoustic echo and noise reduction,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination A hybrid approach for low-complexity joint acoustic echo and noise reduction,

Reference 46

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:884cd67f0f0d97da0094fb0fd157308a5c9510958687088f3ffdd93a8f868a8f

Observation ca385825-3ee9-4172-ad00-0c44504a8d49 · outbound

This paper cites FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-07-02T01:16:24.558049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:43801df376f7876ad67a3d021c0df55e35da5aa4746a223cff4ebcc35c2db6c5

Observation c9c842e9-6e3d-4d70-8ce0-ee79e86a9c40 · outbound

This paper cites Blind audio bandwidth extension: A diffusion-based zero-shot approach,.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination Blind audio bandwidth extension: A diffusion-based zero-shot approach,

Reference 48

Resolution
unresolved
no resolver link, observed 2026-06-28T12:23:00.921024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:507127b175378fc0b6379999dfd0c90472a21ed5f491fc4f78cbc2e5ad7f2374

Pith citing papers

Observation cc2861ac-2084-4112-b4ac-51364defd785 · inbound

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination cites this paper.

A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-07-02T01:16:24.562747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-28T12:23:00.921024Z digest=sha256:a5907412db3baa04a2afd5a8ad71bf964c8ed0d7961b29a05e9751f7afee8f9d