Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements | Paper Citation Record

Observation d66a6cba-d538-49f9-bd5f-f3456d5cc65c · outbound

This paper cites An overview of voice conversion and its challenges: From statistical modeling to deep learning,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements An overview of voice conversion and its challenges: From statistical modeling to deep learning,

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This paper cites Quatieri, Discrete-time speech signal processing: principles and practice, 1st ed.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Quatieri, Discrete-time speech signal processing: principles and practice, 1st ed

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This paper cites Holmes and W.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Holmes and W

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This paper cites Jurafsky and J.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Jurafsky and J

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements A review on speech synthesis based on machine learning,

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This paper cites A Survey on Neural Speech Synthesis.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements A Survey on Neural Speech Synthesis

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This paper cites A survey on speech synthesis techniques in indian languages,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements A survey on speech synthesis techniques in indian languages,

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This paper cites Speech synthesis—a critical review of the state of the art,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Speech synthesis—a critical review of the state of the art,

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This paper cites Automatic speech recog- nition: A survey of deep learning techniques and approaches,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Automatic speech recog- nition: A survey of deep learning techniques and approaches,

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This paper cites Overview of voice conversion methods based on deep learning,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Overview of voice conversion methods based on deep learning,

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This paper cites An overview of voice conversion systems,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements An overview of voice conversion systems,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Unresolved cited work

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This paper cites On the speech properties and feature extraction methods in speech emotion recogni- tion,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements On the speech properties and feature extraction methods in speech emotion recogni- tion,

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This paper cites Mel-spectrogram image- based end-to-end audio deepfake detection under channel-mismatched conditions,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Mel-spectrogram image- based end-to-end audio deepfake detection under channel-mismatched conditions,

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This paper cites V ocbench: A neural vocoder benchmark for speech synthesis,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements V ocbench: A neural vocoder benchmark for speech synthesis,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements V oice conversion using gaussian mixture models,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Phonetic pos- teriorgrams for many-to-one voice conversion without parallel data training,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements V oice con- version through vector quantization,

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This paper cites High-quality voice conversion system based on gmm statistical parameters and rbf neural network,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements High-quality voice conversion system based on gmm statistical parameters and rbf neural network,

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This paper cites Design and implementation of voice conversion system based on gmm and ann,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Design and implementation of voice conversion system based on gmm and ann,

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This paper cites Alleviating the over-smoothing problem in gmm-based voice conversion with discriminative training,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Alleviating the over-smoothing problem in gmm-based voice conversion with discriminative training,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Ppg-based singing voice conversion with adversarial representation learning,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Avqvc: One- shot voice conversion by vector quantization with applying contrastive learning,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Dynamic dynamic time warping,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Multimodal voice conversion based on non-negative matrix factorization,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements V oice conversion using dynamic kernel partial least squares regression,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Phoneme independent hmm voice conversion,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements A survey on variational autoencoders from a green ai perspective,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Generative adversarial nets,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Overview of voice conversion methods based on deep learning,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Generative adversarial networks for speech processing: A review,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements A comparative study on variational au- toencoders and generative adversarial networks,

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Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements A spoofing benchmark for the 2018 voice conversion challenge: leveraging from spoofing countermeasures for speech artifact assessment,

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Observation c0c247bc-a1cb-4238-ae02-f8f0a34f0014 · outbound

This paper cites A survey of convo- lutional neural networks: Analysis, applications, and prospects,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements A survey of convo- lutional neural networks: Analysis, applications, and prospects,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.753357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c1f91cb-fa1c-4d71-83bb-f8cb509b185f · outbound

This paper cites Cycle- consistent adversarial networks for non-parallel vocal effort based speaking style conversion,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Cycle- consistent adversarial networks for non-parallel vocal effort based speaking style conversion,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.756876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a0c1ee1-03d2-46ba-9763-4c6117579252 · outbound

This paper cites A log domain pulse model for parametric speech synthesis,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements A log domain pulse model for parametric speech synthesis,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.760449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8415bc41-5342-4449-b98e-0005dd2ea389 · outbound

This paper cites Spectrum and prosody con- version for cross-lingual voice conversion with cyclegan,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Spectrum and prosody con- version for cross-lingual voice conversion with cyclegan,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.763808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8edd795b-95ea-49bb-a516-ea8dc6899ffb · outbound

This paper cites The fast continuous wavelet transformation (fcwt) for real-time, high-quality, noise-resistant time– frequency analysis,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements The fast continuous wavelet transformation (fcwt) for real-time, high-quality, noise-resistant time– frequency analysis,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.767563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 075b1660-5305-493e-b87c-3a6ce0c5e4c1 · outbound

This paper cites Cyclegan-vc2: Improved cyclegan-based non-parallel voice conversion,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Cyclegan-vc2: Improved cyclegan-based non-parallel voice conversion,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.771117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 812ff885-48d1-4377-8995-3030dcbc593b · outbound

This paper cites Patch-Based Image Inpainting with Generative Adversarial Networks.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Patch-Based Image Inpainting with Generative Adversarial Networks

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.774019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2f4e595-4092-4ad4-8c26-3c557b16d5b5 · outbound

This paper cites Cyclegan-vc-gp: Improved cyclegan-based non-parallel voice conversion,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Cyclegan-vc-gp: Improved cyclegan-based non-parallel voice conversion,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.777366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee2afc36-bea0-41c5-8d96-292de3601631 · outbound

This paper cites Cyclegan-vc3: Ex- amining and improving cyclegan-vcs for mel-spectrogram conversion,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Cyclegan-vc3: Ex- amining and improving cyclegan-vcs for mel-spectrogram conversion,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.780254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f7e216f-3361-4518-97de-9417be98ddd2 · outbound

This paper cites MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.783247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da12e9b9-8b09-49f9-8c07-d385842048b9 · outbound

This paper cites Maskcyclegan-vc: Learning non-parallel voice conversion with filling in frames,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Maskcyclegan-vc: Learning non-parallel voice conversion with filling in frames,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:16.101277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 86572362-3181-4dc6-a18b-022ba431fec0 · outbound

This paper cites Image Fine-grained Inpainting.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Image Fine-grained Inpainting

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.789881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c434e8b-f084-4780-a879-eeed1fdf913f · outbound

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

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements On the study of generative adversarial networks for cross-lingual voice conversion,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:16.090525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 10f8b65d-cba0-43e3-9489-166fe8a7e8c2 · outbound

This paper cites Cstr vctk corpus: English multi-speaker corpus for cstr voice cloning toolkit (version 0.92),.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Cstr vctk corpus: English multi-speaker corpus for cstr voice cloning toolkit (version 0.92),

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:16.081435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 304f7948-29c3-4f3e-9cde-76ad5a5c8242 · outbound

This paper cites The cmu arctic speech databases,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements The cmu arctic speech databases,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:16.071871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 157d831c-f42f-422f-9c25-8996f1390f45 · outbound

This paper cites V oice conversion challenge 2020 intra lingual semi-parallel and cross-lingual voice conversion,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements V oice conversion challenge 2020 intra lingual semi-parallel and cross-lingual voice conversion,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:16.061793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d0c74f0c-bf4b-49f9-ab4e-f8a2edf29973 · outbound

This paper cites Cross-lingual voice conversion with bilingual phonetic posteriorgram and average modeling,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Cross-lingual voice conversion with bilingual phonetic posteriorgram and average modeling,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.807405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ce7c68b-ed6c-4ddf-89ba-775c7b9d45da · outbound

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

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements On the use of i-vectors and average voice model for voice conversion without parallel data,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:16.050348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b1591d35-62af-43fb-b182-83b9e72f4f1c · outbound

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

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements On the study of generative adversarial networks for cross-lingual voice conversion,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:16.039394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d3341987-b504-45b5-8728-2aa9d900a5ef · outbound

This paper cites The blizzard challenge 2010,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements The blizzard challenge 2010,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:16.026644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c307d2d7-0cc5-4521-9644-29ee863b09cf · outbound

This paper cites Cross-lingual voice conversion using a cyclic variational auto-encoder and a wavenet vocoder,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Cross-lingual voice conversion using a cyclic variational auto-encoder and a wavenet vocoder,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:16.015563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0cad8912-9a62-469a-8bcb-37bc9d01ecd0 · outbound

This paper cites Universal neural vocoding with parallel wavenet,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Universal neural vocoding with parallel wavenet,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:16.005154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6b946576-bb42-4c12-a67d-15a50bd9f9da · outbound

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

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Stargan: Unified generative adversarial networks for multi-domain image-to- image translation,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.994999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3720abc1-30e4-4e2d-89a3-30d601510b28 · outbound

This paper cites Optimization of cross-lingual voice conversion with linguistics losses to reduce foreign accents,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Optimization of cross-lingual voice conversion with linguistics losses to reduce foreign accents,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.984054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation daa6c6be-86d8-4218-bda4-0da28e678040 · outbound

This paper cites Cross-gender voice conversion with constant f0-ratio and average background conversion model,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Cross-gender voice conversion with constant f0-ratio and average background conversion model,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.974188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a9dfcd4a-12a3-45a0-afb4-1efea4102b05 · outbound

This paper cites Cross-gender and age speech conversion using hidden markov model based on cepstral coefficients conversion,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Cross-gender and age speech conversion using hidden markov model based on cepstral coefficients conversion,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.964397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 32d96c54-53af-48cb-9aac-3be95c746061 · outbound

This paper cites C-cycletransgan: A non-parallel controllable cross-gender voice conversion model with cyclegan and transformer,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements C-cycletransgan: A non-parallel controllable cross-gender voice conversion model with cyclegan and transformer,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.953394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5ae5456b-4085-4f59-b73a-698d401439b7 · outbound

This paper cites Manipulating voice attributes by adversarial learning of structured disentangled representations,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Manipulating voice attributes by adversarial learning of structured disentangled representations,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.939726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3067d362-40b5-48dd-b23e-a66ced8ec7bb · outbound

This paper cites Fader Networks: Manipulating Images by Sliding Attributes.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Fader Networks: Manipulating Images by Sliding Attributes

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.852995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5d16980-fd52-4a0e-9969-310ff94f03b0 · outbound

This paper cites Stargan-vc: non- parallel many-to-many voice conversion using star generative adversar- ial networks,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Stargan-vc: non- parallel many-to-many voice conversion using star generative adversar- ial networks,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.926287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 92dfd788-cc84-4a76-b9b2-86e6a2ea44a5 · outbound

This paper cites Autoencoding beyond pixels using a learned similarity metric,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Autoencoding beyond pixels using a learned similarity metric,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.913505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1f32bb58-31a9-4787-8004-87a8362f7c13 · outbound

This paper cites StarGAN-VC2: Re- thinking Conditional Methods for StarGAN-Based V oice Conversion,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements StarGAN-VC2: Re- thinking Conditional Methods for StarGAN-Based V oice Conversion,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.901682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 19450646-257c-4cdb-95fa-d7b12e71e083 · outbound

This paper cites A Learned Representation For Artistic Style.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements A Learned Representation For Artistic Style

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.870477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec1e7f75-2730-45c5-830a-5fbdcd74ae4f · outbound

This paper cites Non- Parallel Many-to-Many V oice Conversion with PSR-StarGAN,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Non- Parallel Many-to-Many V oice Conversion with PSR-StarGAN,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.890249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1632fd02-fc44-423e-bcab-82cf1a35803d · outbound

This paper cites StarGANv2-VC: A Diverse, Unsupervised, Non-Parallel Framework for Natural-Sounding V oice Conversion,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements StarGANv2-VC: A Diverse, Unsupervised, Non-Parallel Framework for Natural-Sounding V oice Conversion,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.879721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0aab85d8-ca89-40d1-b44c-56014f17d454 · outbound

This paper cites JVS corpus: free Japanese multi-speaker voice corpus.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements JVS corpus: free Japanese multi-speaker voice corpus

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-16T06:02:14.880919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9d006cf-94b0-48bb-9263-dd96c33e9a05 · outbound

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

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Seen and unseen emotional style transfer for voice conversion with a new emotional speech dataset,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.869942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1e5aed75-139b-41f9-83a0-1444ea8f988c · outbound

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

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Autovc: Zero-shot voice style transfer with only autoencoder loss,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.858244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f9c89f86-e273-49fa-aa24-8107835500b8 · outbound

This paper cites F0- consistent many-to-many non-parallel voice conversion via conditional autoencoder,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements F0- consistent many-to-many non-parallel voice conversion via conditional autoencoder,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.844108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c236b000-d5ee-404d-beb6-46a10c1b00fc · outbound

This paper cites Many-to-Many V oice Conversion Using Cycle-Consistent Variational Autoencoder with Mul- tiple Decoders,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Many-to-Many V oice Conversion Using Cycle-Consistent Variational Autoencoder with Mul- tiple Decoders,

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.830100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f2950acb-85af-4933-93da-167ffde7069e · outbound

This paper cites Wasserstein generative ad- versarial networks,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Wasserstein generative ad- versarial networks,

Reference 102

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.817637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1a52450a-6adf-44b6-9f10-a877daa75182 · outbound

This paper cites V oice Conversion from Unaligned Corpora Using Variational Autoencoding Wasserstein Generative Adversarial Networks,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements V oice Conversion from Unaligned Corpora Using Variational Autoencoding Wasserstein Generative Adversarial Networks,

Reference 103

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.806186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 50f17a70-e49f-4b91-867b-258d39ca1d19 · outbound

This paper cites Fast Learning for Non-Parallel Many-to-Many V oice Conversion with Residual Star Generative Adversarial Networks,.

Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements Fast Learning for Non-Parallel Many-to-Many V oice Conversion with Residual Star Generative Adversarial Networks,

Reference 104

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:02:15.793779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers