Paper Citation Record · LEDGER

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement

As of 11 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2501.13372.

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

pith.paper-citation-record.v1
2501.13372 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:15:49.630165Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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verified fuzzy25
unresolved9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a8b56282-4bae-46e3-bc66-06c80343c1bf · outbound

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

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Robust speech recognition via large-scale weak supervision,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:50.105320Z

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Observation ace06ba6-00a3-471c-8448-0f93efb8381f · outbound

This paper cites Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:50.093576Z

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Observation 556021c8-0bb1-4cf2-8e40-483d685a34c2 · outbound

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

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement YourTTS: Towards zero-shot multi-speaker TTS and zero-shot voice conversion for everyone,

Reference 3

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Observation f11c3f42-3efe-4103-8466-bbfd6fe585c5 · outbound

This paper cites XTTS: A massively multilingual zero-shot text-to-speech model,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement XTTS: A massively multilingual zero-shot text-to-speech model,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:50.072279Z

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Observation 76e75cc2-3f2f-4cc7-baf1-8af9735ac706 · outbound

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

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Reference 5

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Observation 0c1a3863-287c-4e2b-96b5-671ca32e8c17 · outbound

This paper cites Metric- GAN+: An improved version of MetricGAN for speech enhancement,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Metric- GAN+: An improved version of MetricGAN for speech enhancement,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:50.062785Z

Source-reported events for the cited work

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Observation 180b4d37-40be-4afe-9aa4-d2572b0d894e · outbound

This paper cites MANNER: Multi-view attention netwfork for noise erasure,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement MANNER: Multi-view attention netwfork for noise erasure,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:50.052797Z

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Observation 3920a48e-359d-4083-a7e3-8ae60ca84acf · outbound

This paper cites Text is all you need: Personalizing ASR models using controllable speech synthesis,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Text is all you need: Personalizing ASR models using controllable speech synthesis,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:50.041005Z

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Observation e5f5fa13-285e-4f18-9eb8-3c28b9f06753 · outbound

This paper cites The potential of neural speech synthesis-based data augmentation for personalized speech en- hancement,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement The potential of neural speech synthesis-based data augmentation for personalized speech en- hancement,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:50.029364Z

Source-reported events for the cited work

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Observation 171e1ed6-028a-48ff-ba2d-94b149dcaaa7 · outbound

This paper cites A survey on image data augmen- tation for deep learning,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement A survey on image data augmen- tation for deep learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:50.017399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bdb7f0cb-e38c-4ef1-8560-fc0930e0f4ed · outbound

This paper cites Effec- tive data augmentation with diffusion models,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Effec- tive data augmentation with diffusion models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.999133Z

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Observation 1e7ab5a0-53d2-4c7c-88e1-8644a2f3c81a · outbound

This paper cites SpecAug- ment: A simple data augmentation method for automatic speech recog- nition,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement SpecAug- ment: A simple data augmentation method for automatic speech recog- nition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.987157Z

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Observation 01399470-8c03-4018-a842-ada66b1a92fb · outbound

This paper cites Latent filling: Latent space data augmentation for zero-shot speech synthesis,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Latent filling: Latent space data augmentation for zero-shot speech synthesis,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.975500Z

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Observation 51fd7b0b-799e-44db-bfac-039c883e2769 · outbound

This paper cites Improving few-shot learning for talking face system with TTS data augmentation,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Improving few-shot learning for talking face system with TTS data augmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.965190Z

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Observation 9d0549c9-50ff-473d-9843-c3a2659cc45d · outbound

This paper cites Utilizing TTS synthesized data for efficient development of keyword spotting model,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Utilizing TTS synthesized data for efficient development of keyword spotting model,

Reference 15

Resolution: verified exact; doi, observed 2026-08-10T16:15:49.761403Z

Source-reported events for the cited work

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Observation 2d5ad68b-3911-4680-a076-0fba34876169 · outbound

This paper cites Zero shot text to speech augmentation for automatic speech recognition on low-resource accented speech corpora,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Zero shot text to speech augmentation for automatic speech recognition on low-resource accented speech corpora,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.955486Z

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Observation 212694fd-6d07-43c8-9e2e-a203a706cf36 · outbound

This paper cites LibriTTS: A corpus derived from librispeech for text-to-speech,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement LibriTTS: A corpus derived from librispeech for text-to-speech,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.945620Z

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Observation 9d3977ab-c3a0-4707-8268-821acafd00f4 · outbound

This paper cites MUSAN: A Music, Speech, and Noise Corpus.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement MUSAN: A Music, Speech, and Noise Corpus

Reference 18

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Observation 8b78eaca-73ef-45c9-aadc-bd0af01f1c44 · outbound

This paper cites SpeechBrain: A General-Purpose Speech Toolkit.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement SpeechBrain: A General-Purpose Speech Toolkit

Reference 19

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Observation fbb3bceb-93f3-4cd5-ab94-d3f9226851d5 · outbound

This paper cites Deep MOS predictor for synthetic speech using cluster-based modeling,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Deep MOS predictor for synthetic speech using cluster-based modeling,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.934442Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 625a1673-ed59-4411-8211-9ad2eed1aa11 · outbound

This paper cites NISQA: A deep CNN- self-attention model for multidimensional speech quality prediction with crowdsourced datasets,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement NISQA: A deep CNN- self-attention model for multidimensional speech quality prediction with crowdsourced datasets,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.923282Z

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Observation c795598e-c890-4187-8a0e-f3216730d570 · outbound

This paper cites The singing voice conversion challenge 2023,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement The singing voice conversion challenge 2023,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.911509Z

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Observation c639dc55-a213-4604-ac35-39f4e088b1c0 · outbound

This paper cites NaturalSpeech 3: Zero- shot speech synthesis with factorized codec and diffusion models,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement NaturalSpeech 3: Zero- shot speech synthesis with factorized codec and diffusion models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.898964Z

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Observation 890cdb57-1aff-4c5e-bdb1-09b130093fd9 · outbound

This paper cites The T05 System for The VoiceMOS Challenge 2024: Transfer Learning from Deep Image Classifier to Naturalness MOS Prediction of High-Quality Synthetic Speech.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement The T05 System for The VoiceMOS Challenge 2024: Transfer Learning from Deep Image Classifier to Naturalness MOS Prediction of High-Quality Synthetic Speech

Reference 24

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Observation 08c96b53-870f-4f32-9512-dc95a7f84e63 · outbound

This paper cites The VoiceMOS Challenge 2024: Beyond Speech Quality Prediction.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement The VoiceMOS Challenge 2024: Beyond Speech Quality Prediction

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T16:15:49.593554Z

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Observation db4be3dd-d394-487f-9bed-8960f3d47368 · outbound

This paper cites Performance measurement in blind audio source separation,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Performance measurement in blind audio source separation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.887332Z

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Observation c6554e69-ab31-4d57-8fdc-c7bd0c94e3da · outbound

This paper cites A short- time objective intelligibility measure for time-frequency weighted noisy speech,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement A short- time objective intelligibility measure for time-frequency weighted noisy speech,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.874560Z

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This paper cites Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.861940Z

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Observation c6a2707d-6491-4b1b-a958-6aaa0f69ce91 · outbound

This paper cites SpeechT5: Unified- modal encoder-decoder pre-training for spoken language processing,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement SpeechT5: Unified- modal encoder-decoder pre-training for spoken language processing,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.849985Z

Source-reported events for the cited work

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Observation e5b17c5a-2f26-4484-8557-15f96cbfbb5f · outbound

This paper cites Better speech synthesis through scaling.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Better speech synthesis through scaling

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T16:15:49.613067Z

Source-reported events for the cited work

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Observation 3d8ff40b-ea82-4085-9bc9-32a6358b1e7c · outbound

This paper cites Conv-TasNet: Surpassing ideal time–frequency magnitude masking for speech separation,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Conv-TasNet: Surpassing ideal time–frequency magnitude masking for speech separation,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T16:15:49.617374Z

Source-reported events for the cited work

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Observation 98817ee4-eb23-4804-ae9e-80c760431438 · outbound

This paper cites Efficient personalized speech enhancement through self-supervised learning,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Efficient personalized speech enhancement through self-supervised learning,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.831804Z

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Observation 45348eb1-c64e-4037-8f54-614b632690f6 · outbound

This paper cites Librispeech: An ASR corpus based on public domain audio books,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Librispeech: An ASR corpus based on public domain audio books,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:15:49.820175Z

Source-reported events for the cited work

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Observation c76526c2-9544-4905-ae19-7cdeef27dc3c · outbound

This paper cites FSD50K: An open dataset of human-labeled sound events,.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement FSD50K: An open dataset of human-labeled sound events,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T16:15:49.627093Z

Source-reported events for the cited work

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Observation 491f2fd1-750f-431c-9def-71cab089c4d5 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement Adam: A Method for Stochastic Optimization

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T16:15:49.630165Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.