Paper Citation Record · LEDGER

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models

As of 5 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2606.27627.

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

pith.paper-citation-record.v1
2606.27627 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T00:56:43.991936Z

measured 58 of 58 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.

Source: paper_references, paper_reference_links, observed 2026-06-29T00:56:43.991936Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-29T01:02:56.259099Z

Reference resolution

57 of 57 outbound references displayed

verified exact10
verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1396cd70-52b3-4130-897f-96e121f66f6d · outbound

This paper cites Human language perfectly illustrates this duality.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Human language perfectly illustrates this duality

Reference 1

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Observation d1950611-ba4f-412e-998c-9638ef854634 · outbound

This paper cites HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models

Reference 2

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Observation 6b77d89a-8956-4bd8-94b8-ae45e20d060f · outbound

This paper cites Preliminaries: The FocalCodec Architecture FocalCodec [21] employs an asymmetric VQ-V AE architecture centered around a compressor-quantizer-decompressor bottle- neck.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Preliminaries: The FocalCodec Architecture FocalCodec [21] employs an asymmetric VQ-V AE architecture centered around a compressor-quantizer-decompressor bottle- neck

Reference 3

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This paper cites While we train on both thecleanandother(distorted) subsets for training, we strictly limit our evaluation to thecleantest set to maintain con- sistency.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models While we train on both thecleanandother(distorted) subsets for training, we strictly limit our evaluation to thecleantest set to maintain con- sistency

Reference 4

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Observation b81de799-cc3b-486a-97d8-02d8b390cd80 · outbound

This paper cites In both scenarios, we compare our hybrid ap- proach against discrete-only baselines.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models In both scenarios, we compare our hybrid ap- proach against discrete-only baselines

Reference 5

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Observation ab02ad10-8b95-4a05-a14e-2c841dbae95c · outbound

This paper cites By combining discrete tokens with a non-autoregressive residual pathway, we recovered high- fidelity speech details at an ultra-low temporal resolution of 6.25 Hz.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models By combining discrete tokens with a non-autoregressive residual pathway, we recovered high- fidelity speech details at an ultra-low temporal resolution of 6.25 Hz

Reference 6

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Observation add55530-0e75-4f67-a0c9-12d6c2916c43 · outbound

This paper cites LLMs have not been used to author text for the paper, except BibTeX formatting and grammar/wording revisions.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models LLMs have not been used to author text for the paper, except BibTeX formatting and grammar/wording revisions

Reference 7

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Observation e6e197f8-66d1-43f0-9b72-2d34228674d9 · outbound

This paper cites Samir Sadok was supported by the VisaSpeech Inria Associated Team initiative.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Samir Sadok was supported by the VisaSpeech Inria Associated Team initiative

Reference 8

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This paper cites 2011, Neural Comput., 23, 1661, 10.1162/NECO\_a\_00142.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models 2011, Neural Comput., 23, 1661, 10.1162/NECO\_a\_00142

Reference 9

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Observation 87c879d8-b963-4f94-9a6f-ce42a068ff01 · outbound

This paper cites Attractor and integrator networks in the brain,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Attractor and integrator networks in the brain,

Reference 10

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Observation 8992c3d6-15f1-432a-ae37-1976651b248f · outbound

This paper cites Attention is all you need,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Attention is all you need,

Reference 11

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This paper cites Available: https://proceedings.neurips.cc/paper/ 2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Available: https://proceedings.neurips.cc/paper/ 2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html

Reference 12

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Observation 0b7523c3-5237-4c40-80e3-db5fea85b694 · outbound

This paper cites Lan- guage models are few-shot learners,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Lan- guage models are few-shot learners,

Reference 13

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Observation c64e2864-52aa-49c8-a899-3aea54ce7905 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 14

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Observation 27cd7f68-e3d1-4292-b91e-bec4f8c6fade · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 15

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Observation 85c32e3a-c3b5-4362-93aa-9dc259173fbb · outbound

This paper cites Neural discrete represen- tation learning,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Neural discrete represen- tation learning,

Reference 16

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Observation f08b5e23-139b-47bf-995f-b0a0a37c2bc3 · outbound

This paper cites Discrete audio tokens: More than a survey!.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Discrete audio tokens: More than a survey!

Reference 17

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Observation 540cfd5a-6c5f-4f2c-bf84-b415042b991c · outbound

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

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Moshi: a speech-text foundation model for real-time dialogue,

Reference 18

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Observation fd42ae6a-8ec9-42bf-88e9-607fb22c2833 · outbound

This paper cites Available: http://kyutai.org/Moshi.pdf.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Available: http://kyutai.org/Moshi.pdf

Reference 19

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Observation e122b4f2-9d6d-4d05-8cb9-f5be44f59c89 · outbound

This paper cites BigCodec: Pushing the Limits of Low-Bitrate Neural Speech Codec.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models BigCodec: Pushing the Limits of Low-Bitrate Neural Speech Codec

Reference 20

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Observation 5d2969a1-f330-4ccb-8171-eb5121f4d6cd · outbound

This paper cites High-Fidelity Audio Compression with Improved RVQGAN.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models High-Fidelity Audio Compression with Improved RVQGAN

Reference 21

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Observation 6ad35e76-e87e-4e20-87cf-06009e901466 · outbound

This paper cites Audiolm: a language modeling approach to audio gener- ation,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Audiolm: a language modeling approach to audio gener- ation,

Reference 22

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Observation 2a6dc408-4768-4514-a0cf-a7f52250d2ba · outbound

This paper cites Neural codec lan- guage models are zero-shot text to speech synthesizers,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Neural codec lan- guage models are zero-shot text to speech synthesizers,

Reference 23

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Observation 050462e5-03e9-4d0b-8e4c-9c0db67aac9e · outbound

This paper cites SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities

Reference 24

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Observation 271f33de-60cf-41a3-92ff-2bd7d8365366 · outbound

This paper cites SUPERB: Speech Processing Universal PER- formance Benchmark,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models SUPERB: Speech Processing Universal PER- formance Benchmark,

Reference 25

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Observation 535477f0-7465-4ce5-a84e-0926c2dbf303 · outbound

This paper cites DASB - discrete audio and speech benchmark,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models DASB - discrete audio and speech benchmark,

Reference 26

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Observation f81e9906-fbc7-48d2-b33d-e21087082546 · outbound

This paper cites DASB - Discrete Audio and Speech Benchmark.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models DASB - Discrete Audio and Speech Benchmark

Reference 27

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Observation 89a5f7bf-cfe4-4d3e-be39-55c2f5ca66a6 · outbound

This paper cites Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs

Reference 28

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Observation ba7cebae-51c7-4511-b3e8-6a6dfd4bac52 · outbound

This paper cites Modeling strategies for speech enhancement in the latent space of a neural audio codec,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Modeling strategies for speech enhancement in the latent space of a neural audio codec,

Reference 29

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Observation 6150a132-e04b-47f8-9c76-c110ad196745 · outbound

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HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Unresolved cited work

Reference 30

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This paper cites Coding theorems for a discrete source with a fidelity criterion,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Coding theorems for a discrete source with a fidelity criterion,

Reference 31

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This paper cites FocalCodec: Low-bitrate speech coding via focal modulation networks,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models FocalCodec: Low-bitrate speech coding via focal modulation networks,

Reference 32

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HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Focalcodec-stream: Streaming low-bitrate speech coding via causal distillation

Reference 33

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Observation fd8ec7c8-7c02-4686-b147-396e908092a1 · outbound

This paper cites LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech,

Reference 34

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Observation 83abe1ca-728e-49cc-89c9-98df787f9fc2 · outbound

This paper cites Comparing Dis- crete and Continuous Space LLMs for Speech Recognition,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Comparing Dis- crete and Continuous Space LLMs for Speech Recognition,

Reference 35

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This paper cites Clear: Continuous latent autoregressive mod- eling for high-quality and low-latency speech synthesis,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Clear: Continuous latent autoregressive mod- eling for high-quality and low-latency speech synthesis,

Reference 36

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This paper cites Speech synthesis from continuous features using per-token latent diffusion,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Speech synthesis from continuous features using per-token latent diffusion,

Reference 37

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This paper cites Residual to- kens enhance masked autoencoders for speech modeling,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Residual to- kens enhance masked autoencoders for speech modeling,

Reference 38

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This paper cites HyAR: Addressing discrete-continuous action rein- forcement learning via hybrid action representation,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models HyAR: Addressing discrete-continuous action rein- forcement learning via hybrid action representation,

Reference 39

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This paper cites Mixed deep reinforcement learning considering discrete-continuous hybrid action space for smart home energy management,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Mixed deep reinforcement learning considering discrete-continuous hybrid action space for smart home energy management,

Reference 40

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This paper cites Learning insertion primitives with discrete- continuous hybrid action space for robotic assembly tasks,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Learning insertion primitives with discrete- continuous hybrid action space for robotic assembly tasks,

Reference 41

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HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models CANDI: Hybrid Discrete-Continuous Diffusion Models

Reference 42

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HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Image and video tokenization with binary spher- ical quantization,

Reference 43

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This paper cites V ocos: Closing the gap between time-domain and fourier-based neural vocoders for high-quality audio synthesis,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models V ocos: Closing the gap between time-domain and fourier-based neural vocoders for high-quality audio synthesis,

Reference 44

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

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models High-fidelity audio compression with improved rvqgan,

Reference 45

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HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models AdaSpeech: Adaptive text to speech for custom voice,

Reference 46

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HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models ECAPA-TDNN Embeddings for Speaker Diarization,

Reference 47

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This paper cites Open-source conversational AI with SpeechBrain 1.0,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Open-source conversational AI with SpeechBrain 1.0,

Reference 48

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This paper cites An alternative family of transformations,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models An alternative family of transformations,

Reference 49

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This paper cites Librispeech: An ASR corpus based on pub- lic domain audio books,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Librispeech: An ASR corpus based on pub- lic domain audio books,

Reference 50

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HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models UTMOS: UTokyo-SaruLab System for V oice- MOS Challenge 2022,

Reference 51

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This paper cites Pooneh Mousavi, Gallil Maimon, Adel Moumen, Darius Petermann, Jiatong Shi, Haibin Wu, Haici Yang, Anastasia Kuznetsova, Artem Ploujnikov, Ricard Marxer, et al.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Pooneh Mousavi, Gallil Maimon, Adel Moumen, Darius Petermann, Jiatong Shi, Haibin Wu, Haici Yang, Anastasia Kuznetsova, Artem Ploujnikov, Ricard Marxer, et al

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

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Robust speech recognition via large-scale weak supervision,

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Observation 92d8060e-bfd9-48d6-8578-6bb0246cebcc · outbound

This paper cites ChatGPT: Optimizing language models for dialogue,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models ChatGPT: Optimizing language models for dialogue,

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This paper cites Microsoft Copilot,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Microsoft Copilot,

Reference 55

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This paper cites Claude 3 model family technical report,.

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Claude 3 model family technical report,

Reference 56

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HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models Asta: Ai research assis- tant for scientific discovery,

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Pith citing papers

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