Paper Citation Record · LEDGER

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges

As of 7 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2507.00324.

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

pith.paper-citation-record.v1
2507.00324 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:23:53.153699Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:23:45.554040Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T17:26:22.148036Z

Reference resolution

51 of 51 outbound references displayed

verified exact4
verified fuzzy24
unresolved23
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ec41e0e6-1bdb-490c-9c9d-924a5e4a3f48 · outbound

This paper cites This high quality of synthesized speech and the ability to distribute it through so- cial media platforms are giving rise to manipulated information in the digital ecosystem.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges This high quality of synthesized speech and the ability to distribute it through so- cial media platforms are giving rise to manipulated information in the digital ecosystem

Reference 1

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Observation f6badd89-b359-41b2-abf5-00ce9b129525 · outbound

This paper cites Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges

Reference 2

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Observation 34a76118-16bc-4390-b374-e4e12431aa93 · outbound

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Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Unresolved cited work

Reference 3

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Observation ef2e435e-1bf9-447f-b5bf-7d23e788857f · outbound

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Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Unresolved cited work

Reference 4

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Observation 0cb07594-6495-4010-8f5f-3ddfbfd46961 · outbound

This paper cites It uses ffmpeg to download the best audio available in the W A V format, and resample it 16 kHz.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges It uses ffmpeg to download the best audio available in the W A V format, and resample it 16 kHz

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:58.593590Z

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Observation 1b89340b-2c8d-421c-b373-261544e841bf · outbound

This paper cites This step eliminates cross-talk and background speakers.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges This step eliminates cross-talk and background speakers

Reference 6

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Observation 538b5387-e11c-4005-ad38-1da8854d1d6d · outbound

This paper cites We also experimented with the Google speech recognition api package and other commer- cial tools; however, they generated text with less accuracy and without proper punctuation.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges We also experimented with the Google speech recognition api package and other commer- cial tools; however, they generated text with less accuracy and without proper punctuation

Reference 7

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Observation 4c6b9b41-3a0c-48ba-b550-de8cd6d9fb70 · outbound

This paper cites The Biden Deepfake Robocall Is Only the Beginning,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges The Biden Deepfake Robocall Is Only the Beginning,

Reference 8

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Observation a2d27f96-3a9b-4336-98aa-fb4c88df9e0b · outbound

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Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Unresolved cited work

Reference 9

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Observation 4e2b14f0-ca40-4164-8436-31dbb8330c30 · outbound

This paper cites Al- though early models produced robotic-sounding speech despite extensive training data, SSL-based methods significantly im- proved quality through large-scale pre-training.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Al- though early models produced robotic-sounding speech despite extensive training data, SSL-based methods significantly im- proved quality through large-scale pre-training

Reference 10

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Observation db3e056c-b005-4bf6-be5d-33f6bd23d504 · outbound

This paper cites We train StyleTTS2 [22] only using this approach.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges We train StyleTTS2 [22] only using this approach

Reference 11

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Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Unresolved cited work

Reference 12

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Observation d3b137ea-e398-49a9-8790-0dbbb28585d5 · outbound

This paper cites This approach preserved linguistic coherence and improved phoneme alignment, leading to reduced noise and more ac- curate spectrogram generation.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges This approach preserved linguistic coherence and improved phoneme alignment, leading to reduced noise and more ac- curate spectrogram generation

Reference 13

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Observation c11b0556-b91d-47d2-b848-ecabd713c468 · outbound

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Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Unresolved cited work

Reference 14

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Observation 7963db6e-35d6-4ed1-8b50-8ea095bc9efe · outbound

This paper cites For subjective evaluation, we implemented a web-based listening test with 32 unique participants.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges For subjective evaluation, we implemented a web-based listening test with 32 unique participants

Reference 15

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Observation 7c026615-d60c-4f4e-b76d-e32cb31a84dd · outbound

This paper cites Both datasets are derived from the VCTK corpus, which comprises high-quality speech recordings collected in a controlled laboratory environment.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Both datasets are derived from the VCTK corpus, which comprises high-quality speech recordings collected in a controlled laboratory environment

Reference 16

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Observation b33136b0-dcd8-4c67-b4de-efbb7d05da97 · outbound

This paper cites A Survey on Neural Speech Synthesis.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges A Survey on Neural Speech Synthesis

Reference 17

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Observation 8341b7aa-fa69-4608-9424-d0d851b6f797 · outbound

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

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges FastSpeech 2: Fast and High-Quality End-to-End Text to Speech

Reference 18

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Observation 42a44163-1117-4c28-a7f1-e42f8587f90f · outbound

This paper cites Yourtts: Towards zero-shot multi-speaker tts and zero-shot voice conversion for everyone,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Yourtts: Towards zero-shot multi-speaker tts and zero-shot voice conversion for everyone,

Reference 19

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Observation 013f9f70-da0e-40f7-ad16-7ecb4c6027bb · outbound

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

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Reference 20

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Observation b4c32037-b3df-447c-a59e-bf9d1295845a · outbound

This paper cites Zse-vits: A zero-shot expressive voice cloning method based on vits,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Zse-vits: A zero-shot expressive voice cloning method based on vits,

Reference 21

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Observation f633927c-eb74-431a-9d15-b2a2f1f499a7 · outbound

This paper cites Global Risks Report 2024.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Global Risks Report 2024

Reference 22

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Observation 0646b28d-b632-4307-8403-ed1575d8c012 · outbound

This paper cites Russian War Report: Hacked news program and deep- fake video spread false Zelenskyy claims,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Russian War Report: Hacked news program and deep- fake video spread false Zelenskyy claims,

Reference 23

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Observation e58cadbc-45e2-4815-8c6e-4857dd46ce3a · outbound

This paper cites Sadiq Khan says fake AI audio of him nearly led to serious disorder,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Sadiq Khan says fake AI audio of him nearly led to serious disorder,

Reference 24

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Observation ec2c84af-dc38-4b29-94ad-3daabec6004d · outbound

This paper cites Asvspoof 2019: A large-scale public database of synthe- sized, converted and replayed speech,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Asvspoof 2019: A large-scale public database of synthe- sized, converted and replayed speech,

Reference 25

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This paper cites ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild,

Reference 26

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Observation 6d8b812b-97c3-4995-af76-0fcc54dedc96 · outbound

This paper cites Asvspoof 2021: accelerating progress in spoofed and deep- fake speech detection,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Asvspoof 2021: accelerating progress in spoofed and deep- fake speech detection,

Reference 27

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This paper cites Asvspoof 5: crowd- sourced speech data, deepfakes, and adversarial attacks at scale,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Asvspoof 5: crowd- sourced speech data, deepfakes, and adversarial attacks at scale,

Reference 28

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This paper cites MLS: A Large-Scale Multilingual Dataset for Speech Research.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges MLS: A Large-Scale Multilingual Dataset for Speech Research

Reference 29

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This paper cites Is audio spoof detection robust to laundering attacks?.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Is audio spoof detection robust to laundering attacks?

Reference 30

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Observation 98d7743b-a51d-4fd3-90cc-2ec3146a1361 · outbound

This paper cites Dfadd: The diffusion and flow- matching based audio deepfake dataset,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Dfadd: The diffusion and flow- matching based audio deepfake dataset,

Reference 31

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Observation 202f6993-73f5-4b3c-8b6d-764782f87ef3 · outbound

This paper cites The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio

Reference 32

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This paper cites Mlaad: The multi- language audio anti-spoofing dataset,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Mlaad: The multi- language audio anti-spoofing dataset,

Reference 33

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Observation 016cbdbd-4287-4066-bc7e-543d76fab3e5 · outbound

This paper cites Does audio deepfake detection generalize?.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Does audio deepfake detection generalize?

Reference 34

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Observation cd5d01b6-2b31-4925-a019-4ed5511686c0 · outbound

This paper cites Spoofceleb: Speech deepfake detection and sasv in the wild,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Spoofceleb: Speech deepfake detection and sasv in the wild,

Reference 35

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Observation 285295d0-de4a-4fb1-8bc4-32d9917fa255 · outbound

This paper cites V oxceleb: Large-scale speaker verification in the wild,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges V oxceleb: Large-scale speaker verification in the wild,

Reference 36

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Observation 3d92ef0a-b118-4ee8-92f0-503f3c09a367 · outbound

This paper cites Styletts 2: Towards human-level text-to-speech through style dif- fusion and adversarial training with large speech language mod- els,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Styletts 2: Towards human-level text-to-speech through style dif- fusion and adversarial training with large speech language mod- els,

Reference 37

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This paper cites XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model

Reference 38

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This paper cites F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching

Reference 39

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Observation 298bbffc-0c0e-4256-9e0a-b1091923898c · outbound

This paper cites E2 tts: Embarrassingly easy fully non-autoregressive zero-shot tts,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges E2 tts: Embarrassingly easy fully non-autoregressive zero-shot tts,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:54.634215Z

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Observation be5a0f09-b588-4d3b-be6d-6f1fa0693359 · outbound

This paper cites Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesis.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesis

Reference 41

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Observation 02eeabf4-9992-46e8-b95d-3e9fb890efc7 · outbound

This paper cites SSR-Speech: Towards Stable, Safe and Robust Zero-shot Text-based Speech Editing and Synthesis.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges SSR-Speech: Towards Stable, Safe and Robust Zero-shot Text-based Speech Editing and Synthesis

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:23:53.712615Z

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Observation 824d47ee-60fe-4859-b56e-c26291a5af5e · outbound

This paper cites MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer

Reference 43

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Observation 9ae2daa9-6aba-4670-8a54-0512751c1724 · outbound

This paper cites Cosyvoice 2: Scalable streaming speech synthesis with large language models,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Cosyvoice 2: Scalable streaming speech synthesis with large language models,

Reference 44

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Observation a0716cfd-1be5-4404-b1eb-a91a8fb0ecfa · outbound

This paper cites Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis

Reference 46

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Source-reported events for the cited work

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Observation 09561bf6-690f-4de4-9bec-0780c6c14941 · outbound

This paper cites Tacotron: Towards End-to-End Speech Synthesis.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Tacotron: Towards End-to-End Speech Synthesis

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T21:23:52.397089Z

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Observation 089e98bd-4114-4964-bf2b-ecf40630e816 · outbound

This paper cites Glow-tts: A genera- tive flow for text-to-speech via monotonic alignment search,.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Glow-tts: A genera- tive flow for text-to-speech via monotonic alignment search,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:54.495771Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 416732da-24e4-4e09-9e70-d2114e6776df · outbound

This paper cites HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T21:23:52.752336Z

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Observation c8af0324-78ea-4ecb-b44b-104148aad9b4 · outbound

This paper cites UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T21:23:52.972604Z

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Observation 1dd4bc21-92bd-4520-bd50-7f7ad9e468a6 · outbound

This paper cites Deep Learning Based Assessment of Synthetic Speech Naturalness.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges Deep Learning Based Assessment of Synthetic Speech Naturalness

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:23:53.453912Z

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Observation 94fc393c-e3fe-4e56-996a-d6340c2ed578 · outbound

This paper cites CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models.

Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T21:23:52.083979Z

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Pith citing papers

Observation f6badd89-b359-41b2-abf5-00ce9b129525 · inbound