Paper Citation Record · LEDGER

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder

As of 13 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:1907.10185.

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

pith.paper-citation-record.v1
1907.10185 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T17:05:25.448346Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:56:25.416182Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:56:27.474999Z

Reference resolution

43 of 43 outbound references displayed

verified exact9
verified fuzzy29
unresolved5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7afe52cf-54c5-43d6-84b9-c09e996ac605 · outbound

This paper cites an unresolved cited work.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Unresolved cited work

Reference 1

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This paper cites an unresolved cited work.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Unresolved cited work

Reference 2

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Observation c5a4dc7c-1fcd-449c-ac46-0a4e840e8477 · outbound

This paper cites an unresolved cited work.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Unresolved cited work

Reference 3

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Observation 1d2b9336-4dfa-4dcf-8030-ab06089f8dda · outbound

This paper cites Experimental conditions We used a subset of the V oice Conversion Challenge (VCC) 2018 [25] dataset, which included four speakers, i.e., SF1, SM1, TF1, and TM1.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Experimental conditions We used a subset of the V oice Conversion Challenge (VCC) 2018 [25] dataset, which included four speakers, i.e., SF1, SM1, TF1, and TM1

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.300787Z

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Observation 8d2451c8-c1e6-4739-ae72-a33152a6d57c · outbound

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Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Unresolved cited work

Reference 5

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Observation 56274037-0f7f-4ba5-8197-f0a5030ad489 · outbound

This paper cites an unresolved cited work.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Unresolved cited work

Reference 6

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Observation 7cfc75b8-e6e9-45d5-b531-a64ac7b4ed6d · outbound

This paper cites Spectral voice conversion for te xt- to-speech synthesis.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Spectral voice conversion for te xt- to-speech synthesis

Reference 7

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Observation aa4fa8db-f7af-4212-a241-997c5e5a0aa0 · outbound

This paper cites Intra-gender st atistical singing voice conversion with direct waveform modiﬁcation using log-spectral differential.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Intra-gender st atistical singing voice conversion with direct waveform modiﬁcation using log-spectral differential

Reference 8

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Observation 73e361af-13a6-439d-ba5a-c8724a37a0ae · outbound

This paper cites Improving the intelligibility of dy sarthric speech.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Improving the intelligibility of dy sarthric speech

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.374997Z

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Observation 7aa8d878-0455-46cb-b4f0-d5a108454447 · outbound

This paper cites A hybrid approach to electrolaryngeal speech enhancement ba sed on spectral subtraction and statistical voice conversion.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder A hybrid approach to electrolaryngeal speech enhancement ba sed on spectral subtraction and statistical voice conversion

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.365201Z

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Observation 04d741c6-bfd3-4de4-8e9d-50fe3e76810a · outbound

This paper cites Data-driven emotion conversi on in spoken English.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Data-driven emotion conversi on in spoken English

Reference 11

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Observation 481d7fa2-435f-4088-ace5-d31f324518af · outbound

This paper cites Evaluation of expressive speech syn- thesis with voice conversion and copy resynthesis techniqu es.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Evaluation of expressive speech syn- thesis with voice conversion and copy resynthesis techniqu es

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.373846Z

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Observation ae5ef68c-85a7-4ba3-89cd-a5e2b00059b7 · outbound

This paper cites Multisens ory processing for speech enhancement and magnitude-normaliz ed spectra for speech modeling.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Multisens ory processing for speech enhancement and magnitude-normaliz ed spectra for speech modeling

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.369921Z

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Observation ac77c53c-9a20-4b9f-8436-6ceb1cd9056a · outbound

This paper cites Statistical voice con- version techniques for body-conducted unvoiced speech enh ance- ment.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Statistical voice con- version techniques for body-conducted unvoiced speech enh ance- ment

Reference 14

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Observation 543ff8c1-5258-4c34-b29a-17209fa6a6b6 · outbound

This paper cites Articulatory co ntrol- lable speech modiﬁcation based on statistical inversion an d pro- duction mappings.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Articulatory co ntrol- lable speech modiﬁcation based on statistical inversion an d pro- duction mappings

Reference 15

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Observation 5ee73356-5b3b-4ec9-9300-65494494a0a5 · outbound

This paper cites Continuous probabilis- tic transform for voice conversion.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Continuous probabilis- tic transform for voice conversion

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.365695Z

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Observation 95f38400-ba4e-4fb3-a61b-f31669468605 · outbound

This paper cites V oice conversion ba sed on maximum-likelihood estimation of spectral parameter tr ajec- tory.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder V oice conversion ba sed on maximum-likelihood estimation of spectral parameter tr ajec- tory

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.301330Z

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Observation 8e699dcf-b4d0-49d2-81f8-dea767660890 · outbound

This paper cites INCA algorithm fo r training voice conversion systems from nonparallel corpor a.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder INCA algorithm fo r training voice conversion systems from nonparallel corpor a

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.361623Z

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Observation fa25c7e4-fecf-410f-a44b-04d5f428523c · outbound

This paper cites Non-parallel voi ce con- version using joint optimization of alignment by temporal c ontext and spectral distortion.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Non-parallel voi ce con- version using joint optimization of alignment by temporal c ontext and spectral distortion

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.371652Z

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Observation e592f7ab-7dda-4ed6-a724-3a965dfc33f6 · outbound

This paper cites Text-independen t voice conversion based on state mapped codebook.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Text-independen t voice conversion based on state mapped codebook

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.404635Z

Source-reported events for the cited work

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Observation 1aa0a2c3-4901-4732-b4cd-cd4f3859d7b8 · outbound

This paper cites Non-parallel training f or voice conversion based on adaptation method.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Non-parallel training f or voice conversion based on adaptation method

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.368452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8a987472-dd8a-4f9c-9557-1f3ee10f863d · outbound

This paper cites Non-parallel training in voice conversion using an adaptive restricted boltzmann ma- chine.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Non-parallel training in voice conversion using an adaptive restricted boltzmann ma- chine

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.378332Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4ae0fa1a-b449-4176-b4c2-962c3a101978 · outbound

This paper cites High- quality nonparallel voice conversion based on cycle-consistent ad- versarial network.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder High- quality nonparallel voice conversion based on cycle-consistent ad- versarial network

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.381511Z

Source-reported events for the cited work

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Observation ca8e745c-5a33-40c6-9b30-e4d8329dfd6c · outbound

This paper cites StarGAN- VC: Non-parallel many-to-many voice conversion with star gene rative adversarial networks.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder StarGAN- VC: Non-parallel many-to-many voice conversion with star gene rative adversarial networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.401756Z

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Observation 3ad6ab95-45e8-4248-b7c4-0334f7389779 · outbound

This paper cites V oice conversion from non-parallel corpora using variati onal auto-encoder.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder V oice conversion from non-parallel corpora using variati onal auto-encoder

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.354661Z

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Observation 0b1bd2ed-0ff7-4ec7-937d-ed3098509f2b · outbound

This paper cites V oice conversion from unaligned corpora using variationa l au- toencoding Wasserstein generative adversarial networks.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder V oice conversion from unaligned corpora using variationa l au- toencoding Wasserstein generative adversarial networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.358000Z

Source-reported events for the cited work

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Observation 93344cad-0003-4ab1-9526-0d50768bab8c · outbound

This paper cites Non-p arallel voice conversion using variational autoencoders conditio ned by phonetic posteriorgrams and d-vectors.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Non-p arallel voice conversion using variational autoencoders conditio ned by phonetic posteriorgrams and d-vectors

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.351457Z

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Observation b6b52295-540f-4f73-a280-9106c3d24fd4 · outbound

This paper cites ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-24T17:06:16.894374Z

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Observation 475f1a54-24ac-4262-9108-30e7de53016b · outbound

This paper cites Auto-Encoding Variational Bayes.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Auto-Encoding Variational Bayes

Reference 29

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Observation ac333cb3-ea3e-43f6-9f93-a7d7102bb0ce · outbound

This paper cites Learning Correspondence from the Cycle-Consistency of Time.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Learning Correspondence from the Cycle-Consistency of Time

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-24T17:06:16.900312Z

Source-reported events for the cited work

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Observation ad3bf3bc-db46-435f-98ed-2d6505085a77 · outbound

This paper cites The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-24T17:06:16.928446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 01936bcc-5027-4e76-8081-95df3aa19577 · outbound

This paper cites WORLD: A vocoder- based high-quality speech synthesis system for real-time a ppli- cations.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder WORLD: A vocoder- based high-quality speech synthesis system for real-time a ppli- cations

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.315391Z

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Observation c5e63c56-3cb8-4b02-b15a-52264ba2ce96 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-24T17:06:16.943078Z

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Observation f97c494a-5bbb-4031-992b-c77fe54fa31d · outbound

This paper cites Dropout: a simple way to prevent neural n et- works from overﬁtting.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Dropout: a simple way to prevent neural n et- works from overﬁtting

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.388188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 06d30510-301b-45c4-968a-93e904d45508 · outbound

This paper cites Understanding the difﬁculty o f train- ing deep feedforward neural networks.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Understanding the difﬁculty o f train- ing deep feedforward neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.339488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7ca64cec-e622-4f3b-9a12-1ef5d270fc9e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Adam: A Method for Stochastic Optimization

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-24T17:06:16.934899Z

Source-reported events for the cited work

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Observation d39892ff-56f8-47a1-b7c1-e5175aa34016 · outbound

This paper cites Unsupervised speech representation learning using WaveNet autoencoders.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Unsupervised speech representation learning using WaveNet autoencoders

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-24T17:06:16.950097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a8021571-3b66-44dc-b857-19a5e04bb670 · outbound

This paper cites Learning Latent Representations for Speech Generation and Transformation.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Learning Latent Representations for Speech Generation and Transformation

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-24T17:06:16.893473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f3090437-cd63-45d0-8663-018f7ef8547e · outbound

This paper cites Neural discrete represe ntation learning.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Neural discrete represe ntation learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.390361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cce8c504-97a5-4548-990c-aa6a466f3921 · outbound

This paper cites Unsupervised learning of dis- entangled and interpretable representations from sequent ial data.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Unsupervised learning of dis- entangled and interpretable representations from sequent ial data

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.332846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b3b75764-43b8-4d6f-895e-6eed82028d40 · outbound

This paper cites Non- parallel voice conversion using i-vector PLDA: Towards unifying spe aker veriﬁcation and transformation.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder Non- parallel voice conversion using i-vector PLDA: Towards unifying spe aker veriﬁcation and transformation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.393534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bd8dd9f0-b5fc-4035-98ab-d92bf4eef222 · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder WaveNet: A Generative Model for Raw Audio

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-05-24T17:06:16.923305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f7a99c0e-5b02-4614-a0bd-4fcb7fabbb59 · outbound

This paper cites V oice conversion with cyclic recurrent neural network and ﬁne- tuned WaveNet vocoder.

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder V oice conversion with cyclic recurrent neural network and ﬁne- tuned WaveNet vocoder

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T17:06:17.398267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation 701efda3-043e-4175-b5f6-48add114b86f · 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 Non-Parallel Voice Conversion with Cyclic Variational Autoencoder

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:56:27.522823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.