Paper Citation Record · LEDGER

A Variational Framework for Improving Naturalness in Generative Spoken Language Models

As of 10 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2506.14767.

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

pith.paper-citation-record.v1
2506.14767 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:15:45.148144Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T22:36:21.635792Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T22:40:43.524737Z

Reference resolution

55 of 55 outbound references displayed

verified exact2
verified fuzzy21
unresolved31
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ba1e0b1e-8c72-470e-883e-3c2d34136503 · outbound

This paper cites The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.678703Z

Source-reported events for the cited work

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Observation f2a6e95a-cd6c-44f3-852b-6107383c8b37 · outbound

This paper cites Audiolm: A language modeling approach to audio generation.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Audiolm: A language modeling approach to audio generation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.686691Z

Source-reported events for the cited work

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Observation 7081adfd-e962-4bae-8224-f7250c0f310b · outbound

This paper cites R., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., and Bengio, S.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models R., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., and Bengio, S

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.694626Z

Source-reported events for the cited work

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Observation 9f61c2dd-2a54-4237-a64a-74f2d07545c2 · outbound

This paper cites A vector quantized approach for text to speech synthesis on real-world spontaneous speech.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models A vector quantized approach for text to speech synthesis on real-world spontaneous speech

Reference 4

Resolution: verified exact; doi, observed 2026-08-07T00:15:45.456626Z

Source-reported events for the cited work

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Observation b77a3c44-563f-4113-9052-de17d230f94b · outbound

This paper cites Wavlm: Large-scale self-supervised pre-training for full stack speech processing.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Wavlm: Large-scale self-supervised pre-training for full stack speech processing

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.711037Z

Source-reported events for the cited work

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Observation 564ecf6b-fd82-4b41-b60e-b9bf15fb71bc · outbound

This paper cites VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.715062Z

Source-reported events for the cited work

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Observation 89de81df-8587-459f-866e-90107bc36d87 · outbound

This paper cites Neural codec language models are zero-shot text to speech synthesizers.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Neural codec language models are zero-shot text to speech synthesizers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.719272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ebd119f-6a03-4b11-ad4e-e0f3d1dc3ba3 · outbound

This paper cites High fidelity neural audio compression.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models High fidelity neural audio compression

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.246973Z

Source-reported events for the cited work

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Observation cde48c6a-a147-4d50-b57b-8ced722801bb · outbound

This paper cites Density estimation using real NVP.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Density estimation using real NVP

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.230593Z

Source-reported events for the cited work

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Observation 76296d15-6b1d-4f77-a815-ea0fe44a5d52 · outbound

This paper cites LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.732151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d48f864-0325-4e74-8888-1f56b95b740d · outbound

This paper cites Moshi: a speech-text foundation model for real-time dialogue.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Moshi: a speech-text foundation model for real-time dialogue

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.737743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca1acb72-cb5d-46c0-96ac-d265c4e1dacb · outbound

This paper cites R., Schuller, B.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models R., Schuller, B

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.218086Z

Source-reported events for the cited work

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Observation d193aad3-7f07-40cc-8916-e05d2be7e331 · outbound

This paper cites Cyclical annealing schedule: A simple approach to mitigating KL vanishing.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Cyclical annealing schedule: A simple approach to mitigating KL vanishing

Reference 13

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Source-reported events for the cited work

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Observation 0d6769a3-93da-4b33-bb26-ed499e4c2394 · outbound

This paper cites A., Gat, I., Conneau, A., Kreuk, F., Copet, J., Defossez, A., Synnaeve, G., Dupoux, E., Schwartz, R., and Adi, Y.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models A., Gat, I., Conneau, A., Kreuk, F., Copet, J., Defossez, A., Synnaeve, G., Dupoux, E., Schwartz, R., and Adi, Y

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.203129Z

Source-reported events for the cited work

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Observation b9c97a94-90df-4789-96a5-e8211f254aa4 · outbound

This paper cites and Gimpel, K.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models and Gimpel, K

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.191551Z

Source-reported events for the cited work

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Observation 698c1489-ce5d-4a3d-ae2a-37e220a2dbb3 · outbound

This paper cites beta- VAE : Learning basic visual concepts with a constrained variational framework.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models beta- VAE : Learning basic visual concepts with a constrained variational framework

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.178622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 93b6eb26-fadb-4ee1-86ed-11e7a50b624f · outbound

This paper cites Denoising diffusion probabilistic models.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Denoising diffusion probabilistic models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.163238Z

Source-reported events for the cited work

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Observation 5399a6ff-35fc-46a3-8a72-4035d2294f04 · outbound

This paper cites H., Lakhotia, K., Salakhutdinov, R., and Mohamed, A.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models H., Lakhotia, K., Salakhutdinov, R., and Mohamed, A

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.767005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4ff4c7b-9797-48ac-9d62-c757a7a281e6 · outbound

This paper cites Libri-light: A benchmark for ASR with limited or no supervision.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Libri-light: A benchmark for ASR with limited or no supervision

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.148703Z

Source-reported events for the cited work

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Observation 3e0fef10-d4a0-4037-a8d8-1791feefc7c7 · outbound

This paper cites A., Riviere, M., Mohamed, A., Dupoux, E., and Hsu, W.-N.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models A., Riviere, M., Mohamed, A., Dupoux, E., and Hsu, W.-N

Reference 20

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Source-reported events for the cited work

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Observation b25007a2-782c-44e2-aa25-61a3ed16ad22 · outbound

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

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Glow-tts: A generative flow for text-to-speech via monotonic alignment search

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.126895Z

Source-reported events for the cited work

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Observation c3534bbe-3196-4cbc-a46a-2700ccb22eaf · outbound

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

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.102107Z

Source-reported events for the cited work

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Observation ecd4c332-ee4b-450a-9065-e6a0a012edb8 · outbound

This paper cites W., Salamon, J., Li, P., and Bello, J.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models W., Salamon, J., Li, P., and Bello, J

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.811638Z

Source-reported events for the cited work

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Observation 759585a2-6a73-4d2f-8e28-20e96b1913aa · outbound

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A Variational Framework for Improving Naturalness in Generative Spoken Language Models Unresolved cited work

Reference 24

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Source-reported events for the cited work

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Observation 9939882c-0785-4730-93e0-b7aec7842f83 · outbound

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

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.088472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c5aef27-0c33-4419-b661-b77d5c82c445 · outbound

This paper cites On generative spoken language modeling from raw audio.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models On generative spoken language modeling from raw audio

Reference 26

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Source-reported events for the cited work

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Observation 788a30d6-ce08-4c10-98e1-02cbc78266dc · outbound

This paper cites and Hutter, F.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models and Hutter, F

Reference 27

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Observation 09122a09-c6f1-4d73-bc4e-ac17bd61a597 · outbound

This paper cites Voxtlm: Unified decoder-only models for consolidating speech recognition, synthesis and speech, text continuation tasks.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Voxtlm: Unified decoder-only models for consolidating speech recognition, synthesis and speech, text continuation tasks

Reference 28

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Observation b3dfe765-ef91-4123-97e4-f790772ba959 · outbound

This paper cites The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.865712Z

Source-reported events for the cited work

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Observation b5b906e3-3692-4993-a309-9a45dc5f3820 · outbound

This paper cites GPT-4 Technical Report.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models GPT-4 Technical Report

Reference 30

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Observation a40358ec-9d19-4c10-8439-2ee44a0c04d2 · outbound

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

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Librispeech: An ASR corpus based on public domain audio books

Reference 31

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Observation f4f05302-5d31-4840-bb16-4c470fb3169e · outbound

This paper cites FiLM : Visual reasoning with a general conditioning layer.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models FiLM : Visual reasoning with a general conditioning layer

Reference 32

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Observation 6161288b-832b-4021-8b9f-338a826f5ee1 · outbound

This paper cites Train short, test long: Attention with linear biases enables input length extrapolation.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Train short, test long: Attention with linear biases enables input length extrapolation

Reference 33

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Observation 68b61674-7217-4229-8eb5-a2286a4006fa · outbound

This paper cites W., Xu, T., Brockman, G., Mcleavey, C., and Sutskever, I.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models W., Xu, T., Brockman, G., Mcleavey, C., and Sutskever, I

Reference 34

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Observation 8d0a9955-4f6c-4e3b-b365-c57975b17953 · outbound

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A Variational Framework for Improving Naturalness in Generative Spoken Language Models and Torre, R

Reference 35

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Observation 1f065bbb-92a8-42b2-af87-0012358b00fb · outbound

This paper cites and Mohamed, S.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models and Mohamed, S

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:47.004749Z

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Observation b6086208-4561-4e62-a19c-57ce05649417 · outbound

This paper cites U-Net : Convolutional networks for biomedical image segmentation.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models U-Net : Convolutional networks for biomedical image segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:46.981677Z

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Observation 250938e1-419f-42c5-8657-6eeebc8e36a0 · outbound

This paper cites AudioPaLM: A Large Language Model That Can Speak and Listen.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models AudioPaLM: A Large Language Model That Can Speak and Listen

Reference 38

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Observation ca725f33-4967-4d55-8d71-7663d8635d77 · outbound

This paper cites The interspeech 2009 emotion challenge.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models The interspeech 2009 emotion challenge

Reference 39

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Observation cf588aef-9ebe-4026-b8c7-f318b7b238e1 · outbound

This paper cites The interspeech 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models The interspeech 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism

Reference 40

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Observation 50183299-26c3-43e0-956c-9f0784219d60 · outbound

This paper cites Denoising diffusion implicit models.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Denoising diffusion implicit models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:46.948606Z

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Observation 5d9a725c-12f5-428e-a78a-29c0ef522c8a · outbound

This paper cites J., Cao, Y., Zen, H., Rosenberg, A., Ramabhadran, B., and Wu, Y.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models J., Cao, Y., Zen, H., Rosenberg, A., Ramabhadran, B., and Wu, Y

Reference 42

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Observation 50697df9-0e5b-420f-993f-35ef51bc8b7e · outbound

This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Instance Normalization: The Missing Ingredient for Fast Stylization

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.968933Z

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Observation a02db15b-48f2-4f0c-9a22-08874b0ee3a5 · outbound

This paper cites and Kautz, J.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models and Kautz, J

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:46.931145Z

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Observation b23bd89d-c775-4353-b9d6-bfd3ddf53469 · outbound

This paper cites Learning de-identified representations of prosody from raw audio.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Learning de-identified representations of prosody from raw audio

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:46.915433Z

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Observation 0a438fe2-7ef9-48ee-9747-3270430b9103 · outbound

This paper cites On layer normalization in the transformer architecture.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models On layer normalization in the transformer architecture

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:46.897756Z

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Observation af5c8ce8-8abd-4d61-a155-cb6747b226f9 · outbound

This paper cites CSTR VCTK Corpus : English multi-speaker corpus for CSTR voice cloning toolkit (version 0.92), 2019.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models CSTR VCTK Corpus : English multi-speaker corpus for CSTR voice cloning toolkit (version 0.92), 2019

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:44.997848Z

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Observation 23b41b76-da0d-4cb5-84e0-be94071ce7f3 · outbound

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A Variational Framework for Improving Naturalness in Generative Spoken Language Models Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:15:46.880905Z

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Observation 83211cda-db71-4d3b-9aac-3d338593031b · outbound

This paper cites Soundstream: An end-to-end neural audio codec.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Soundstream: An end-to-end neural audio codec

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:45.017442Z

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Observation ff1b8f43-70e1-47ae-9c2b-1067a04d88a1 · outbound

This paper cites and Sennrich, R.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models and Sennrich, R

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:46.864209Z

Source-reported events for the cited work

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Observation c1c6ae91-4d1c-44e4-bfdb-9ac0a89cc508 · outbound

This paper cites SpeechTokenizer : Unified speech tokenizer for speech language models.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models SpeechTokenizer : Unified speech tokenizer for speech language models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:46.849087Z

Source-reported events for the cited work

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Observation ccce29af-1127-41b4-a35f-07f657f13abb · outbound

This paper cites R., Kadav, A., and Graf, H.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models R., Kadav, A., and Graf, H

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:15:46.828978Z

Source-reported events for the cited work

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Observation 82f83d34-0588-4528-8c2b-b1d7d84b1214 · outbound

This paper cites @esa (Ref.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models @esa (Ref

Reference 53

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This paper cites an unresolved cited work.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models Unresolved cited work

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:45.135549Z

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This paper cites The evaluation metrics are detailed in Section ssec:eval-all.

A Variational Framework for Improving Naturalness in Generative Spoken Language Models The evaluation metrics are detailed in Section ssec:eval-all

Reference 55

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Pith citing papers

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Speak Your Mind: The Speech Continuation Task as a Probe of Voice-Based Model Bias cites this paper.

Speak Your Mind: The Speech Continuation Task as a Probe of Voice-Based Model Bias A Variational Framework for Improving Naturalness in Generative Spoken Language Models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-21T22:40:43.526798Z

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