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Paper Citation Record · LEDGER

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2511.01056.

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

pith.paper-citation-record.v1
2511.01056 v3

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:27:32.622659Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:27:30.285078Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

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Outbound references

Observation 2033c47d-d8b4-407a-bf84-b43e0798d82e · outbound

This paper cites WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion

Reference 1

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source=pdf_text observed=2026-08-04T00:27:30.285078Z digest=sha256:804d720693a14b1f2a8b7fd3057dbafe1bcefe1222368836c63e998b96e36d78

Observation 6327563c-8ea8-4817-9ce9-72857de7159c · outbound

This paper cites Overview The proposed whisper-to-speech (W2S) framework comprises three stages, as illustrated in Fig.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Overview The proposed whisper-to-speech (W2S) framework comprises three stages, as illustrated in Fig

Reference 2

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source=pdf_text observed=2026-08-04T00:27:30.440254Z digest=sha256:0f6ef6165d18897cb87e0d7047b925b9b1adc92208d497003a6acfa7b0fbafc2

Observation 4bb93aa6-22ec-457e-958d-9c59077066d8 · outbound

This paper cites an unresolved cited work.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Unresolved cited work

Reference 3

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Observation 83ea74e0-dfff-4670-89f7-f5e78a4d8f60 · outbound

This paper cites Objective evaluations show consistent gains over whispered inputs and performance approaching that of ground-truth recordings in terms of naturalness (DNSMOS 3.11, UTMOS 2.52vs.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Objective evaluations show consistent gains over whispered inputs and performance approaching that of ground-truth recordings in terms of naturalness (DNSMOS 3.11, UTMOS 2.52vs

Reference 4

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source=pdf_text observed=2026-08-04T00:27:30.633764Z digest=sha256:802ed2cc763f65c55aa3deb7901a069e4f3af89079c29f1d012da7f590de6b91

Observation 93ae1124-b765-4d9a-b87f-b3b786023bad · outbound

This paper cites Attention-Guided Generative Adversarial Network for Whisper to Normal Speech Conversion.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Attention-Guided Generative Adversarial Network for Whisper to Normal Speech Conversion

Reference 5

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source=pdf_text observed=2026-08-04T00:27:30.742369Z digest=sha256:c53eaf9a64b18aab7d9bd851e27159747a21497e2aa05c48cb775dcdf7409e02

Observation 030bb5cc-66fb-4cd9-bbca-eee124b96177 · outbound

This paper cites A novel attention-guided generative ad- versarial network for whisper-to-normal speech conversion,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion A novel attention-guided generative ad- versarial network for whisper-to-normal speech conversion,

Reference 6

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source=pdf_text observed=2026-08-04T00:27:30.877727Z digest=sha256:5e6a57d9fee18c19752755d6641c984fe75ae5c23e564748cdb5c9c569a21089

Observation 1a6215b3-b46f-4579-b6ce-c1914bed3cea · outbound

This paper cites End-to-End Whisper to Natural Speech Conversion using Modified Transformer Network.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion End-to-End Whisper to Natural Speech Conversion using Modified Transformer Network

Reference 7

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source=pdf_text observed=2026-08-04T00:27:31.077349Z digest=sha256:1d9000b2bf78a52a67a74dc3775aeaf032894fedc1e10ff4db64e02550471d4b

Observation 2660c0f7-146d-46da-8c7c-55de5fa4a870 · outbound

This paper cites Gener- ative adversarial networks for whispered to voiced speech con- version: a comparative study,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Gener- ative adversarial networks for whispered to voiced speech con- version: a comparative study,

Reference 8

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source=pdf_text observed=2026-08-04T00:27:31.209083Z digest=sha256:4e4d9944fc96362401d710e8551c29e703b60fb76e8e5bdd7fe138d3c8a639a7

Observation ee10939d-fc61-4a92-bc9e-324173ed39a2 · outbound

This paper cites Maskcyclegan-based whisper to normal speech conversion,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Maskcyclegan-based whisper to normal speech conversion,

Reference 9

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Observation e08590eb-faab-4165-879b-1d715e8a37cb · outbound

This paper cites V ocoder-free non-parallel conversion of whispered speech with masked cycle-consistent generative adversarial net- works,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion V ocoder-free non-parallel conversion of whispered speech with masked cycle-consistent generative adversarial net- works,

Reference 10

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Observation bd4b45c9-b9b1-4ea9-a473-2fb6394a360c · outbound

This paper cites Wesper: Zero-shot and realtime whisper to normal voice conversion for whisper-based speech interac- tions,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Wesper: Zero-shot and realtime whisper to normal voice conversion for whisper-based speech interac- tions,

Reference 11

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Observation 01832f1e-6f80-4f34-b96a-89feb01029e7 · outbound

This paper cites Distillw2n: A lightweight one-shot whisper to normal voice conversion model using distillation of self- supervised features,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Distillw2n: A lightweight one-shot whisper to normal voice conversion model using distillation of self- supervised features,

Reference 12

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source=pdf_text observed=2026-08-04T00:27:31.640744Z digest=sha256:81be682b27d6bbe91920fd88f00c0e79a78c5cdd8c106a706c715bc78c4e0cb1

Observation 1a2a0a42-ddca-4696-8201-fbc699ef57aa · outbound

This paper cites Improvement Speaker Similarity for Zero-Shot Any-to-Any Voice Conversion of Whispered and Regular Speech.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Improvement Speaker Similarity for Zero-Shot Any-to-Any Voice Conversion of Whispered and Regular Speech

Reference 13

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source=pdf_text observed=2026-08-04T00:27:31.662280Z digest=sha256:d9b41bed30c3cebffe00bc8feb45c29b49ee7691502117e280383163a94388e2

Observation 894eef0e-9f7d-48dd-887c-61da2d95b4e9 · outbound

This paper cites Whis- pered speech conversion based on the inversion of mel fre- quency cepstral coefficient features,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Whis- pered speech conversion based on the inversion of mel fre- quency cepstral coefficient features,

Reference 14

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Observation 31dae4b7-00d6-4f15-b7a9-e23d9e3303e6 · outbound

This paper cites Glottal flow synthesis for whisper-to-speech conversion,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Glottal flow synthesis for whisper-to-speech conversion,

Reference 15

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Observation 27428f30-a187-4aef-8e21-e3c211174b1b · outbound

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

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Robust speech recognition via large-scale weak supervision,

Reference 16

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Observation 467a1e4a-1c9b-4d7a-86b9-ec7f070fe0ce · outbound

This paper cites Aishell6-whisper: A chinese mandarin audio-visual whisper speech dataset with speech recognition baselines,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Aishell6-whisper: A chinese mandarin audio-visual whisper speech dataset with speech recognition baselines,

Reference 17

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Observation 1aa43ace-e3cb-442d-840a-997ac9e62c34 · outbound

This paper cites Soft-dtw: a differentiable loss function for time-series,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Soft-dtw: a differentiable loss function for time-series,

Reference 18

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Observation 7dac97be-17ba-447e-ad96-257d4e781f18 · outbound

This paper cites FastSpeech 2: Fast and High-Quality End-to-End Text to Speech.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion FastSpeech 2: Fast and High-Quality End-to-End Text to Speech

Reference 19

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source=pdf_text observed=2026-08-04T00:27:32.435469Z digest=sha256:1c1006ed1e85c3e877a1b21de4b577cd6cdcd14cea0fe2e9a938a067c9dfc132

Observation 6d8a2234-0ca5-4a03-b47e-b1c4c88de3c2 · outbound

This paper cites Wespeaker: A research and production oriented speaker embedding learning toolkit,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Wespeaker: A research and production oriented speaker embedding learning toolkit,

Reference 20

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source=pdf_text observed=2026-08-04T00:27:32.459345Z digest=sha256:76b0a3e1838158d8f821d74cf140c63824208562b967f0dfe99c5af59538b54f

Observation 4ea14fc2-563a-4b30-9675-108021d84dff · outbound

This paper cites VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark

Reference 21

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Observation 3945a79a-ecfe-4eac-8e1c-4e45dd8250e5 · outbound

This paper cites VoxCeleb2: Deep Speaker Recognition.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion VoxCeleb2: Deep Speaker Recognition

Reference 22

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Observation de3b0585-e9fd-4f91-b6fe-3b758095457c · outbound

This paper cites Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,

Reference 23

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source=pdf_text observed=2026-08-04T00:27:32.617233Z digest=sha256:3342d15056f4c3fad24872623901b3807e9b0eff32552479254e2820d518634a

Observation 5cbee1b4-df98-485e-891e-deffc5ba766a · outbound

This paper cites Dnsmos: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion Dnsmos: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,

Reference 24

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Observation 1a38ff9c-55ca-4e97-9ee8-0db8bbd969e2 · outbound

This paper cites UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022

Reference 25

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Pith citing papers

Observation 2033c47d-d8b4-407a-bf84-b43e0798d82e · inbound

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion cites this paper.

WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion WhisperVC: Decoupled Cross-Domain Alignment and Speech Generation for Low-Resource Whisper-to-Normal Conversion

Reference 1

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source=pdf_text observed=2026-08-04T00:27:30.285078Z digest=sha256:804d720693a14b1f2a8b7fd3057dbafe1bcefe1222368836c63e998b96e36d78