Paper Citation Record · LEDGER

Representing Speech Through Autoregressive Prediction of Cochlear Tokens

As of 23 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2508.11598.

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

pith.paper-citation-record.v1
2508.11598 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T19:54:58.755219Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T19:54:59.403728Z

Reference resolution

65 of 65 outbound references displayed

verified exact4
verified fuzzy35
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c87c66bf-5d6a-4851-91b1-d73aff9c5ccf · outbound

This paper cites Representing Speech Through Autoregressive Prediction of Cochlear Tokens.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Representing Speech Through Autoregressive Prediction of Cochlear Tokens

Reference 1

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Observation e70517cc-c426-43e4-b519-7ec9eba9316d · outbound

This paper cites water” and “river.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens water” and “river

Reference 2

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Observation 1dbc760f-0472-4349-bb5c-34037b0f7723 · outbound

This paper cites er” was often confused with “r.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens er” was often confused with “r

Reference 3

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Observation 8418ae8c-cc49-47e6-8870-8f8d089400a8 · outbound

This paper cites Transformation Imitation.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Transformation Imitation

Reference 4

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Observation 87b8727c-cc16-4faa-a61c-702bb755647c · outbound

This paper cites acknowledges support from The K.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens acknowledges support from The K

Reference 5

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Observation d35525ca-2a30-43d0-9329-ae571f573311 · outbound

This paper cites SUPERB: Speech processing Universal PERformance Benchmark.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens SUPERB: Speech processing Universal PERformance Benchmark

Reference 6

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Observation 7257bd88-eb8c-483a-84c1-1faded21bf78 · outbound

This paper cites Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models

Reference 7

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Observation ba6f1da7-8ec5-4af4-a722-1c40c19dfc80 · outbound

This paper cites A Review of Deep Learning Techniques for Speech Processing.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens A Review of Deep Learning Techniques for Speech Processing

Reference 8

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Observation 86bff55e-0e93-4447-aa6c-b3938d7f5878 · outbound

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

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Soundstream: An end-to-end neural audio codec,

Reference 9

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Observation 587e6140-14f7-4998-a625-0c5aa2904316 · outbound

This paper cites High Fidelity Neural Audio Compression.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens High Fidelity Neural Audio Compression

Reference 10

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Observation f225f353-cfd7-4e63-b6bc-70213e002bb5 · outbound

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

Representing Speech Through Autoregressive Prediction of Cochlear Tokens HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec

Reference 11

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Observation 4075519f-d176-4b8a-bce3-882d355bb389 · outbound

This paper cites High-fidelity audio compression with improved rvqgan,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens High-fidelity audio compression with improved rvqgan,

Reference 12

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Observation b43b3d0a-1d76-4908-9c67-cf1874151514 · outbound

This paper cites Language-Codec: Bridging Discrete Codec Representations and Speech Language Models.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Language-Codec: Bridging Discrete Codec Representations and Speech Language Models

Reference 13

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Observation 3f67f68b-b935-425c-86b6-f24317671d3e · outbound

This paper cites Speechtok- enizer: Unified speech tokenizer for speech language models,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Speechtok- enizer: Unified speech tokenizer for speech language models,

Reference 14

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Observation 61224e10-6883-4e89-a553-dd944a8b051c · outbound

This paper cites CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech

Reference 15

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Observation 1886f8f4-88fc-4818-9a5b-9a573165cfbb · outbound

This paper cites Spectral Codecs: Improving Non-Autoregressive Speech Synthesis with Spectrogram-Based Audio Codecs.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Spectral Codecs: Improving Non-Autoregressive Speech Synthesis with Spectrogram-Based Audio Codecs

Reference 16

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Observation 7f87b3e5-0f62-484c-a2bf-6ee9e158b49b · outbound

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

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Reference 17

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Observation e7744f64-4ab0-4588-a3cc-55690244c93e · outbound

This paper cites Hierarchical organization of hu- man auditory cortex: evidence from acoustic invariance in the re- sponse to intelligible speech,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Hierarchical organization of hu- man auditory cortex: evidence from acoustic invariance in the re- sponse to intelligible speech,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:55:03.462945Z

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Observation 5d4cc089-2728-4852-b9a9-0427f40f18a8 · outbound

This paper cites Intonational speech prosody encoding in the human auditory cortex,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Intonational speech prosody encoding in the human auditory cortex,

Reference 19

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This paper cites Hubert: Self-supervised speech rep- resentation learning by masked prediction of hidden units,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Hubert: Self-supervised speech rep- resentation learning by masked prediction of hidden units,

Reference 20

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This paper cites W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

Reference 21

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Observation 43f76c7d-ebb6-4dc9-9aa6-e9fbefa6023f · outbound

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

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Wavlm: Large-scale self- supervised pre-training for full stack speech processing,

Reference 22

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This paper cites Blind phoneme segmentation with temporal prediction errors.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Blind phoneme segmentation with temporal prediction errors

Reference 23

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Observation b177be0e-b3b8-4219-b791-7006268441cc · outbound

This paper cites An Unsupervised Autoregressive Model for Speech Representation Learning.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens An Unsupervised Autoregressive Model for Speech Representation Learning

Reference 24

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This paper cites Acquiring language from speech by learning to remember and predict,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Acquiring language from speech by learning to remember and predict,

Reference 25

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This paper cites Vector-Quantized Autoregressive Predictive Coding.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Vector-Quantized Autoregressive Predictive Coding

Reference 26

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Audio albert: A lite bert for self-supervised learning of audio representation,

Reference 27

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This paper cites On generative spoken language modeling from raw au- dio,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens On generative spoken language modeling from raw au- dio,

Reference 28

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Audiolm: a language modeling approach to audio gener- ation,

Reference 29

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This paper cites Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target

Reference 30

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens BERT: Pre- training of Deep Bidirectional Transformers for Language Under- standing,

Reference 31

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This paper cites Representation Learning with Contrastive Predictive Coding.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Representation Learning with Contrastive Predictive Coding

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This paper cites Wav2vec 2.0: a framework for self-supervised learning of speech representa- tions,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Wav2vec 2.0: a framework for self-supervised learning of speech representa- tions,

Reference 33

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Contrastive learning of general-purpose audio representations,

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This paper cites Unsupervised speech segmentation and variable rate repre- sentation learning using segmental contrastive predictive coding,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Unsupervised speech segmentation and variable rate repre- sentation learning using segmental contrastive predictive coding,

Reference 35

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Self-normalization and noise- robustness in early auditory representations,

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Unresolved cited work

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Multiresolution spectrotem- poral analysis of complex sounds,

Reference 38

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Model metamers reveal divergent invariances between biological and ar- tificial neural networks,

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Derivation of auditory filter shapes from notched-noise data,

Reference 40

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This paper cites Sound texture perception via statistics of the auditory periphery: evidence from sound syn- thesis,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Sound texture perception via statistics of the auditory periphery: evidence from sound syn- thesis,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:55:01.847895Z

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This paper cites Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 42

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This paper cites Lib- rispeech: an asr corpus based on public domain audio books,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Lib- rispeech: an asr corpus based on public domain audio books,

Reference 43

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Improving language understanding by generative pre-training,

Reference 44

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Gaussian Error Linear Units (GELUs)

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Root Mean Square Layer Normalization

Reference 46

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Libri-light: A benchmark for asr with limited or no supervision,

Reference 47

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Timit acoustic phonetic continuous speech cor- pus,

Reference 48

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This paper cites Speaker-independent phone recogni- tion using hidden markov models,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Speaker-independent phone recogni- tion using hidden markov models,

Reference 49

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Representing Speech Through Autoregressive Prediction of Cochlear Tokens Scikit-learn: Machine Learning in Python,

Reference 50

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

Representing Speech Through Autoregressive Prediction of Cochlear Tokens The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling

Reference 51

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This paper cites Over-reliance on english hinders cognitive science,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Over-reliance on english hinders cognitive science,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:55:01.002185Z

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Observation 099f2fd0-5e9a-45d3-8cef-f1ff9189d8b0 · outbound

This paper cites To- wards inclusive automatic speech recognition,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens To- wards inclusive automatic speech recognition,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:55:00.891669Z

Source-reported events for the cited work

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This paper cites Say- cam: A large, longitudinal audiovisual dataset recorded from the infant’s perspective,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Say- cam: A large, longitudinal audiovisual dataset recorded from the infant’s perspective,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:55:00.778519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 071b6908-1925-49bf-b58e-757b99b7b746 · outbound

This paper cites Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-05T19:54:58.720381Z

Source-reported events for the cited work

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Observation 19b671da-5252-4497-9d4b-a0caffead11c · outbound

This paper cites A task-optimized neural network repli- cates human auditory behavior, predicts brain responses, and re- veals a cortical processing hierarchy,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens A task-optimized neural network repli- cates human auditory behavior, predicts brain responses, and re- veals a cortical processing hierarchy,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:55:00.644771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d7f67bdb-df30-48c7-bf43-d124bd131507 · outbound

This paper cites Toward a realistic model of speech processing in the brain with self-supervised learning,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Toward a realistic model of speech processing in the brain with self-supervised learning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:55:00.557865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 02a3ce6b-c9d5-4b31-b65d-c42660187c23 · outbound

This paper cites Dissecting neural computations of the human auditory pathway using deep neural networks for speech,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Dissecting neural computations of the human auditory pathway using deep neural networks for speech,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:55:00.429799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aee78f11-cfe0-443e-8813-2da3653d8bce · outbound

This paper cites Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:55:00.297487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 91f4b4e3-66e6-4218-ac8c-0418f1508ccb · outbound

This paper cites Speech taskonomy: Which speech tasks are the most predictive of fmri brain activity?.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Speech taskonomy: Which speech tasks are the most predictive of fmri brain activity?

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:55:00.192596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3aff2a49-132d-4df5-803b-27c0a1a5079a · outbound

This paper cites Language in brains, minds, and machines,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Language in brains, minds, and machines,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:55:00.058048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0264a1d7-45ec-4d1d-a2b8-29e6e9f2387a · outbound

This paper cites Brain-tuned Speech Models Better Reflect Speech Processing Stages in the Brain.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Brain-tuned Speech Models Better Reflect Speech Processing Stages in the Brain

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T19:54:58.744160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3849522-5e98-42e5-b262-13da0475a8e1 · outbound

This paper cites An algorithm for the machine calculation of complex fourier series,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens An algorithm for the machine calculation of complex fourier series,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:54:59.936362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 13352801-f597-4a72-8ac0-fc4301aedecb · outbound

This paper cites Lib- rispeech: An ASR corpus based on public domain audio books,.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens Lib- rispeech: An ASR corpus based on public domain audio books,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:54:59.764253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ec17cb52-6a2a-46f9-a014-6dc96e011ce3 · outbound

This paper cites codebook usage.

Representing Speech Through Autoregressive Prediction of Cochlear Tokens codebook usage

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:54:59.574844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation c87c66bf-5d6a-4851-91b1-d73aff9c5ccf · inbound