Paper Citation Record · LEDGER

Improving speaker verification robustness with synthetic emotional utterances

As of 22 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2412.00319.

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

pith.paper-citation-record.v1
2412.00319 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:35:09.656991Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:35:09.423408Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T05:35:09.716364Z

Reference resolution

49 of 49 outbound references displayed

verified exact0
verified fuzzy41
unresolved6
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 21bb89a5-1d66-419d-988a-74300d4636de · outbound

This paper cites Improving speaker verification robustness with synthetic emotional utterances.

Improving speaker verification robustness with synthetic emotional utterances Improving speaker verification robustness with synthetic emotional utterances

Reference 1

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Observation 87c46476-9958-452c-97e1-d449372869f5 · outbound

This paper cites an unresolved cited work.

Improving speaker verification robustness with synthetic emotional utterances Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:35:10.459249Z

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Observation 43581f7d-46d2-42b0-a1f8-b2d48ba4b6de · outbound

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Improving speaker verification robustness with synthetic emotional utterances Unresolved cited work

Reference 3

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T05:35:10.444486Z

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Observation 2a9e3021-3695-42b6-abd2-f5360b572610 · outbound

This paper cites 50 neutral + 10 angry.

Improving speaker verification robustness with synthetic emotional utterances 50 neutral + 10 angry

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.429315Z

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Improving speaker verification robustness with synthetic emotional utterances Unresolved cited work

Reference 5

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Improving speaker verification robustness with synthetic emotional utterances Unresolved cited work

Reference 6

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Observation 70f34c8b-9fbd-40be-ae47-690eef86e21b · outbound

This paper cites an unresolved cited work.

Improving speaker verification robustness with synthetic emotional utterances Unresolved cited work

Reference 7

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Observation 3e751bc1-c138-4e41-9e5c-a99a1ffb20c1 · outbound

This paper cites A tutorial on text-independent speaker ver- ification,.

Improving speaker verification robustness with synthetic emotional utterances A tutorial on text-independent speaker ver- ification,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.370479Z

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Observation ade90d1d-f56a-4084-830f-c7ad7df534c0 · outbound

This paper cites Speaker indexing in large audio databases using anchor models,.

Improving speaker verification robustness with synthetic emotional utterances Speaker indexing in large audio databases using anchor models,

Reference 9

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Observation 1b907963-e3e9-4da7-b8bc-bcd23f5baece · outbound

This paper cites Speaker verification using support vector machines and high-level features,.

Improving speaker verification robustness with synthetic emotional utterances Speaker verification using support vector machines and high-level features,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.340155Z

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Observation 42f5028b-ad48-4d90-9f50-ca02f61bc3a4 · outbound

This paper cites Rawnet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification,.

Improving speaker verification robustness with synthetic emotional utterances Rawnet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.323765Z

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Observation b0c0c265-0de2-41a9-b0fc-bb5c603234d4 · outbound

This paper cites Speaker diariza- tion with lstm,.

Improving speaker verification robustness with synthetic emotional utterances Speaker diariza- tion with lstm,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.308865Z

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Observation f1edc30b-2dc9-4459-80e7-02139ce3478f · outbound

This paper cites An overview of automatic speaker ver- ification system,.

Improving speaker verification robustness with synthetic emotional utterances An overview of automatic speaker ver- ification system,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.294329Z

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Observation 05d676e3-a8cd-4160-8a49-27ecd3bcf84f · outbound

This paper cites Ap- plications of speaker recognition,.

Improving speaker verification robustness with synthetic emotional utterances Ap- plications of speaker recognition,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.278959Z

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Observation 3905cb56-6282-4ac1-bac5-5fa36a459244 · outbound

This paper cites Emotional speaker identification using a novel capsule nets model,.

Improving speaker verification robustness with synthetic emotional utterances Emotional speaker identification using a novel capsule nets model,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.265080Z

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Observation cd176ab3-c07e-4065-b47f-6d3db9ec1a19 · outbound

This paper cites Wearable emotion recognition using heart rate data from a smart bracelet,.

Improving speaker verification robustness with synthetic emotional utterances Wearable emotion recognition using heart rate data from a smart bracelet,

Reference 16

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Observation 7442e09c-892a-4e0c-a168-600d66122d51 · outbound

This paper cites Physiologi- cal changes associated with emotion,.

Improving speaker verification robustness with synthetic emotional utterances Physiologi- cal changes associated with emotion,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.234566Z

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Observation dc35f617-57bb-43f6-ba6d-9c9e615829ca · outbound

This paper cites X-vectors meet emo- tions: A study on dependencies between emotion and speaker recognition,.

Improving speaker verification robustness with synthetic emotional utterances X-vectors meet emo- tions: A study on dependencies between emotion and speaker recognition,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.219621Z

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Observation c8c66b83-d761-4b2c-9e8a-47a8453483c1 · outbound

This paper cites Achieving fair speech emotion recognition via perceptual fairness,.

Improving speaker verification robustness with synthetic emotional utterances Achieving fair speech emotion recognition via perceptual fairness,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.204776Z

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Observation d57e92a5-7c99-403c-97bf-63c9830f8acd · outbound

This paper cites Gender-related dif- ferences in the production and perception of emotion,.

Improving speaker verification robustness with synthetic emotional utterances Gender-related dif- ferences in the production and perception of emotion,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.189670Z

Source-reported events for the cited work

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Observation 63e9f6cf-5b16-4ba3-aefe-eb3f317af86c · outbound

This paper cites Demographic fair- ness in biometric systems: What do the experts say?,.

Improving speaker verification robustness with synthetic emotional utterances Demographic fair- ness in biometric systems: What do the experts say?,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.173981Z

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Observation 150c01fe-b859-4ac0-93e9-41764d724951 · outbound

This paper cites Effects of emotional valence and arousal on the voice perception network,.

Improving speaker verification robustness with synthetic emotional utterances Effects of emotional valence and arousal on the voice perception network,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.157696Z

Source-reported events for the cited work

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Observation d66c90ab-22a9-4d59-a169-b19e9b2ea32d · outbound

This paper cites Unveiling the acoustic properties that describe the valence dimension,.

Improving speaker verification robustness with synthetic emotional utterances Unveiling the acoustic properties that describe the valence dimension,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.142152Z

Source-reported events for the cited work

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Observation b5ddc1b4-b838-45c4-b62c-1c97f7705048 · outbound

This paper cites Generalized end-to-end loss for speaker verification,.

Improving speaker verification robustness with synthetic emotional utterances Generalized end-to-end loss for speaker verification,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.127102Z

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Observation ebdfe8df-91c7-4124-8971-7beb4628a226 · outbound

This paper cites V oice conversion based on maximum-likelihood estimation of spectral parameter trajectory,.

Improving speaker verification robustness with synthetic emotional utterances V oice conversion based on maximum-likelihood estimation of spectral parameter trajectory,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.111642Z

Source-reported events for the cited work

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Observation c1c60472-9fad-42ef-bfc8-4c6d4aff2050 · outbound

This paper cites Algorithms for non- negative matrix factorization,.

Improving speaker verification robustness with synthetic emotional utterances Algorithms for non- negative matrix factorization,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.095421Z

Source-reported events for the cited work

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Observation 31b10648-71cd-48f2-bd3b-9956a27e5711 · outbound

This paper cites Exemplar-based sparse representation with residual compensation for voice conversion,.

Improving speaker verification robustness with synthetic emotional utterances Exemplar-based sparse representation with residual compensation for voice conversion,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.077571Z

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This paper cites V oice conversion using deep neural networks with layer-wise generative training,.

Improving speaker verification robustness with synthetic emotional utterances V oice conversion using deep neural networks with layer-wise generative training,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.056726Z

Source-reported events for the cited work

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Observation c87f5e2c-6409-4262-bba1-0f1d6357b670 · outbound

This paper cites Spectral mapping using artificial neu- ral networks for voice conversion,.

Improving speaker verification robustness with synthetic emotional utterances Spectral mapping using artificial neu- ral networks for voice conversion,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.040965Z

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Improving speaker verification robustness with synthetic emotional utterances V oice conversion in high-order eigen space using deep belief nets,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.023651Z

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Observation a4eb2015-0786-49dc-89f3-25fd3d45c6f3 · outbound

This paper cites On the use of i-vectors and average voice model for voice conversion without parallel data,.

Improving speaker verification robustness with synthetic emotional utterances On the use of i-vectors and average voice model for voice conversion without parallel data,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:10.006878Z

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This paper cites Non-parallel voice conversion using variational autoencoders conditioned by phonetic poste- riorgrams and d-vectors,.

Improving speaker verification robustness with synthetic emotional utterances Non-parallel voice conversion using variational autoencoders conditioned by phonetic poste- riorgrams and d-vectors,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.991483Z

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This paper cites V oice conversion from non-parallel corpora using variational auto-encoder,.

Improving speaker verification robustness with synthetic emotional utterances V oice conversion from non-parallel corpora using variational auto-encoder,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.975732Z

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Observation 6c7c211b-7ad1-41bd-abfa-5bd9db5fd7bd · outbound

This paper cites Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks.

Improving speaker verification robustness with synthetic emotional utterances Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks

Reference 34

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This paper cites On the study of generative adversarial net- works for cross-lingual voice conversion,.

Improving speaker verification robustness with synthetic emotional utterances On the study of generative adversarial net- works for cross-lingual voice conversion,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.960045Z

Source-reported events for the cited work

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Observation 3680c1a5-fe81-4131-83a6-5f62db17b228 · outbound

This paper cites Cyclegan-vc: Non-parallel voice conversion using cycle-consistent ad- versarial networks,.

Improving speaker verification robustness with synthetic emotional utterances Cyclegan-vc: Non-parallel voice conversion using cycle-consistent ad- versarial networks,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.944910Z

Source-reported events for the cited work

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Observation c524bfeb-239f-4cbb-839c-36c8f09d69c3 · outbound

This paper cites Transform- ing spectrum and prosody for emotional voice conversion with non-parallel training data,.

Improving speaker verification robustness with synthetic emotional utterances Transform- ing spectrum and prosody for emotional voice conversion with non-parallel training data,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.930643Z

Source-reported events for the cited work

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Observation 092f5052-574d-4251-8bf8-510842057772 · outbound

This paper cites Fusion of embeddings net- works for robust combination of text dependent and in- dependent speaker recognition,.

Improving speaker verification robustness with synthetic emotional utterances Fusion of embeddings net- works for robust combination of text dependent and in- dependent speaker recognition,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.915828Z

Source-reported events for the cited work

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Observation 6237d49f-8229-4ea2-ab13-86f41763d00f · outbound

This paper cites Deep neural networks for small footprint text-dependent speaker verifi- cation,.

Improving speaker verification robustness with synthetic emotional utterances Deep neural networks for small footprint text-dependent speaker verifi- cation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.899923Z

Source-reported events for the cited work

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Observation 33972e8f-8bec-48fd-a4f4-e36081910ced · outbound

This paper cites World: A vocoder-based high-quality speech synthesis system for real-time applications,.

Improving speaker verification robustness with synthetic emotional utterances World: A vocoder-based high-quality speech synthesis system for real-time applications,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.884559Z

Source-reported events for the cited work

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Observation f28f1bbb-2362-4f8e-a91c-c9a029e29d88 · outbound

This paper cites Seen and unseen emotional style transfer for voice con- version with a new emotional speech dataset,.

Improving speaker verification robustness with synthetic emotional utterances Seen and unseen emotional style transfer for voice con- version with a new emotional speech dataset,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.868506Z

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Observation 8a5f584b-247f-49aa-be8f-3ad7a89e19e0 · outbound

This paper cites The emotional voices database: Towards controlling the emotion dimension in voice generation systems,.

Improving speaker verification robustness with synthetic emotional utterances The emotional voices database: Towards controlling the emotion dimension in voice generation systems,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.852898Z

Source-reported events for the cited work

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Observation 5fc35a2e-3952-46d3-9faf-b1219e0f546d · outbound

This paper cites The ryer- son audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english,.

Improving speaker verification robustness with synthetic emotional utterances The ryer- son audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.837627Z

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Observation b934f128-f9da-4df7-a8a4-65238fcd6f8a · outbound

This paper cites an unresolved cited work.

Improving speaker verification robustness with synthetic emotional utterances Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:35:09.821960Z

Source-reported events for the cited work

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Observation e51c3bd1-ab9d-4f4f-aef2-0e4a87dddb76 · outbound

This paper cites Towards principled methods for training generative adversarial networks,.

Improving speaker verification robustness with synthetic emotional utterances Towards principled methods for training generative adversarial networks,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.803719Z

Source-reported events for the cited work

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Observation 6dfb2b91-36e5-4852-81e1-5567c0256d51 · outbound

This paper cites Generative adversarial nets,.

Improving speaker verification robustness with synthetic emotional utterances Generative adversarial nets,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.787852Z

Source-reported events for the cited work

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Observation bf22a265-fe85-43da-ba8d-8d749e7a507f · outbound

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

Improving speaker verification robustness with synthetic emotional utterances Adam: A method for stochastic optimization,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.771395Z

Source-reported events for the cited work

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Observation a7c6c734-b527-4815-b848-2a15ec23ca7d · outbound

This paper cites Visualiz- ing data using t-sne,.

Improving speaker verification robustness with synthetic emotional utterances Visualiz- ing data using t-sne,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.755555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 133ceb26-6c49-4840-bfa2-20b4334eaeab · outbound

This paper cites Identifying source speakers for voice conversion based spoofing attacks on speaker verification systems,.

Improving speaker verification robustness with synthetic emotional utterances Identifying source speakers for voice conversion based spoofing attacks on speaker verification systems,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:35:09.739772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 21bb89a5-1d66-419d-988a-74300d4636de · inbound