Paper Citation Record · LEDGER

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training

As of 19 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 3 inbound Pith citation observations for arXiv:2501.04416.

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

pith.paper-citation-record.v1
2501.04416 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:38:27.559518Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:12:57.241564Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-09T06:00:36.712633Z

Reference resolution

33 of 33 outbound references displayed

verified exact1
verified fuzzy26
unresolved5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1df7a0b5-d9fc-47da-b9fe-f511f760defc · outbound

This paper cites Autovc: Zero-shot voice style transfer with only autoencoder loss,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Autovc: Zero-shot voice style transfer with only autoencoder loss,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.984045Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d5e025b4-12bb-4de3-99b2-75e414909a26 · outbound

This paper cites LM-VC: zero-shot voice conversion via speech generation based on language models,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training LM-VC: zero-shot voice conversion via speech generation based on language models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.891618Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 45f85feb-2479-44f8-bcbc-164613bc1013 · outbound

This paper cites Multi-speaker and multi-domain emotional voice conversion using factorized hierarchical variational autoencoder,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Multi-speaker and multi-domain emotional voice conversion using factorized hierarchical variational autoencoder,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.881186Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c92eb10f-b57e-4391-a1df-f6e41954f6e9 · outbound

This paper cites Un- paired image-to-image translation using cycle-consistent adversarial net- works,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Un- paired image-to-image translation using cycle-consistent adversarial net- works,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.870545Z

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Observation ff3d329e-625e-408e-b179-c0ad19ca6c09 · outbound

This paper cites Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.824001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c21a3e6-b082-4ddf-8e8f-2c549c34542b · outbound

This paper cites CV AE-GAN: fine-grained image generation through asymmetric train- ing,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training CV AE-GAN: fine-grained image generation through asymmetric train- ing,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.653702Z

Source-reported events for the cited work

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Observation f0bf70aa-16e5-443c-bcd5-33d9b4846442 · outbound

This paper cites Dis- entanglement of emotional style and speaker identity for expressive voice conversion,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Dis- entanglement of emotional style and speaker identity for expressive voice conversion,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.642700Z

Source-reported events for the cited work

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Observation 71c13269-88fb-46bc-8134-d4f37fdf57f4 · outbound

This paper cites DurFlex-EVC: Duration-Flexible Emotional Voice Conversion Leveraging Discrete Representations without Text Alignment.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training DurFlex-EVC: Duration-Flexible Emotional Voice Conversion Leveraging Discrete Representations without Text Alignment

Reference 8

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Source-reported events for the cited work

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Observation ebb65c78-68f9-4535-93ba-3f922a831b4c · outbound

This paper cites Delivering speaking style in low-resource voice conver- sion with multi-factor constraints,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Delivering speaking style in low-resource voice conver- sion with multi-factor constraints,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.630637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d839277-c962-4bff-8a62-c94d3bc21b89 · outbound

This paper cites Speechsplit2.0: Unsupervised speech disentanglement for voice con- version without tuning autoencoder bottlenecks,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Speechsplit2.0: Unsupervised speech disentanglement for voice con- version without tuning autoencoder bottlenecks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.619230Z

Source-reported events for the cited work

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Observation 025ed1cf-b6ae-4d85-86bf-6bf929519ce9 · outbound

This paper cites METTS: multilingual emotional text-to-speech by cross- speaker and cross-lingual emotion transfer,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training METTS: multilingual emotional text-to-speech by cross- speaker and cross-lingual emotion transfer,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.608502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c9fe511f-ed9b-40b2-a23c-d02eb8393644 · outbound

This paper cites X-vectors: Robust DNN embeddings for speaker recognition,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training X-vectors: Robust DNN embeddings for speaker recognition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.515621Z

Source-reported events for the cited work

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Observation c0c7190d-bc8c-48d9-8c95-28aa2269c5af · outbound

This paper cites Towards improved zero-shot voice conversion with con- ditional DSV AE,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Towards improved zero-shot voice conversion with con- ditional DSV AE,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.474355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation be2425b9-c148-4d31-8b20-688670926d6d · outbound

This paper cites SEF-VC: Speaker Embedding Free Zero-Shot Voice Conversion with Cross Attention.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training SEF-VC: Speaker Embedding Free Zero-Shot Voice Conversion with Cross Attention

Reference 14

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Source-reported events for the cited work

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Observation 5501901c-2b1e-43a2-aee2-62b84516628c · outbound

This paper cites Audiolm: A language modeling approach to audio generation,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Audiolm: A language modeling approach to audio generation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.461424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e4880a84-9395-4c86-a20c-eae6afe12ed3 · outbound

This paper cites Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T21:38:27.158207Z

Source-reported events for the cited work

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Observation 6a4c0181-e05d-4e68-b28c-dcf13b4b7d8f · outbound

This paper cites High Fidelity Neural Audio Compression.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training High Fidelity Neural Audio Compression

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T21:38:27.162462Z

Source-reported events for the cited work

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Observation 0080efa5-e570-4399-9453-3477c3169c63 · outbound

This paper cites Naturalspeech 2: Latent diffusion models are natural and zero-shot speech and singing synthesizers,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Naturalspeech 2: Latent diffusion models are natural and zero-shot speech and singing synthesizers,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.449645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 960db646-fca0-4de2-b32d-543c76c5f376 · outbound

This paper cites Make- an-audio: Text-to-audio generation with prompt-enhanced diffusion mod- els,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Make- an-audio: Text-to-audio generation with prompt-enhanced diffusion mod- els,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.363183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d9b8deb6-9169-493a-983d-d6978419faa3 · outbound

This paper cites Unsupervised speech decomposition via triple informa- tion bottleneck,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Unsupervised speech decomposition via triple informa- tion bottleneck,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.298553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2ab3d7b2-b886-4df1-a761-1ff9efb53034 · outbound

This paper cites SoundStorm: Efficient Parallel Audio Generation.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training SoundStorm: Efficient Parallel Audio Generation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T21:38:27.366566Z

Source-reported events for the cited work

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Observation 4579a6c1-2436-437f-bf65-76a6c560d63e · outbound

This paper cites Neural discrete representation learning,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Neural discrete representation learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.285838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7be335ad-dc9c-43d7-8fff-da3dfe7f1a9c · outbound

This paper cites One-shot voice conversion by separating speaker and content representations with instance normaliza- tion,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training One-shot voice conversion by separating speaker and content representations with instance normaliza- tion,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.271748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 20abf3fc-0e61-4064-95f7-5bf32eb75867 · outbound

This paper cites StyleSinger: Style Transfer for Out-of-Domain Singing Voice Synthesis.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training StyleSinger: Style Transfer for Out-of-Domain Singing Voice Synthesis

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:38:27.690224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1bf60e37-ae4e-4948-ad92-2d1210e06185 · outbound

This paper cites Unsupervised domain adap- tation by backpropagation,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Unsupervised domain adap- tation by backpropagation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.246333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5c30abd7-b04a-407c-868f-5fbcbe03c9de · outbound

This paper cites MLS: A large-scale multilingual dataset for speech research,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training MLS: A large-scale multilingual dataset for speech research,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.055376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2bdae9fb-c9db-414d-9c44-a4a62fef10d9 · outbound

This paper cites Token-level ensemble distillation for grapheme-to- phoneme conversion,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Token-level ensemble distillation for grapheme-to- phoneme conversion,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.043300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b973d6e4-6f0e-4eb4-aefc-9fb13f247a60 · outbound

This paper cites Style tokens: Unsupervised style modeling, control and transfer in end- to-end speech synthesis,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Style tokens: Unsupervised style modeling, control and transfer in end- to-end speech synthesis,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.032611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 05ca7bce-a19c-423a-9cd6-00f7a091d7b4 · outbound

This paper cites Emotional voice conversion: Theory, databases and ESD,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Emotional voice conversion: Theory, databases and ESD,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.020819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ed8c9e68-868f-4a98-a0bf-159634135bc6 · outbound

This paper cites Visualizing data using t-sne,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training Visualizing data using t-sne,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.007498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 36de0868-4381-4f39-95f7-c42d84f6605f · outbound

This paper cites emotion2vec: Self-supervised pre-training for speech emotion representation,.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training emotion2vec: Self-supervised pre-training for speech emotion representation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:27.895176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f42d3be1-0067-486f-afd4-70609f15727a · outbound

This paper cites 37 of JMLR Workshop and Conference Proceedings , pp.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training 37 of JMLR Workshop and Conference Proceedings , pp

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:38:28.110573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 426fdf7d-81c9-4c57-8aca-23013801b639 · outbound

This paper cites 664–668, ISCA.

ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training 664–668, ISCA

Reference 2019

Resolution: parse uncertain; raw_fallback, observed 2026-08-10T21:38:28.259490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 76b09370-129b-436f-93aa-eec1a6c3e74e · inbound

DiffDSR: Dysarthric Speech Reconstruction Using Latent Diffusion Model cites this paper.

DiffDSR: Dysarthric Speech Reconstruction Using Latent Diffusion Model ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T12:12:57.241564Z

Source-reported events for the cited work

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Observation c3b103a8-05ae-4853-9180-1373feef1466 · inbound

ReFlow-VC: Zero-shot Voice Conversion Based on Rectified Flow and Speaker Feature Optimization cites this paper.

ReFlow-VC: Zero-shot Voice Conversion Based on Rectified Flow and Speaker Feature Optimization ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:56:26.433423Z

Source-reported events for the cited work

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Observation 02e20413-4c61-451a-b3d6-628509b92948 · inbound

Mixed-Precision Information Bottlenecks for On-Device Trait-State Disentanglement in Bipolar Agitation Detection cites this paper.

Mixed-Precision Information Bottlenecks for On-Device Trait-State Disentanglement in Bipolar Agitation Detection ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training

Reference 105

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:00:36.715260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.